AWS DEVOPS & AI TECHNICAL PROJECT MANAGER We are seeking an experienced AWS DevOps & AI Technical Project Manager to manage approximately 25 AWS DevOps engineers working simultaneously on multiple AI and cloud-transformation projects. This is a hands-on leadership role for someone who understands AWS architecture, DevOps delivery, and AWS AI services—not a general project manager who only schedules meetings and updates spreadsheets. RESPONSIBILITIES • Manage a distributed team of approximately 25 AWS DevOps engineers • Oversee multiple AWS AI and cloud projects running concurrently • Organize delivery into two-week Agile sprints • Work directly with developers to define tasks, owners, priorities, dependencies, estimates, and acceptance criteria • Confirm that deliverables and deadlines are realistic before committing to clients or leadership • Conduct sprint planning, daily standups, backlog reviews, sprint reviews, and retrospectives • Monitor progress daily and identify schedule risks before deadlines are missed • Proactively resolve technical blockers, staffing constraints, unclear requirements, and cross-team dependencies • Escalate problems promptly with a clear explanation of the issue, impact, available options, and recommended solution • Maintain accurate project plans, sprint boards, documentation, status reports, and delivery forecasts • Coordinate communication among developers, architects, customers, AWS representatives, and company leadership • Ensure that completed work meets technical requirements, acceptance criteria, security standards, and client expectations • Hold team members accountable while maintaining a productive and professional working environment • Improve our project-management processes as the volume and complexity of projects continue to grow REQUIRED AWS AND DEVOPS EXPERIENCE Candidates must have significant practical experience managing AWS and DevOps projects, including familiarity with: • Amazon Bedrock • Amazon Bedrock Agents and AgentCore • Amazon SageMaker • Generative AI and agentic AI architectures • Retrieval-Augmented Generation (RAG) • AWS Lambda • Amazon API Gateway • AWS Step Functions • Amazon EventBridge • Amazon S3 • Amazon DynamoDB • Amazon OpenSearch Service • Amazon ECS, EKS, and EC2 • Infrastructure as Code using Terraform, AWS CloudFormation, or AWS CDK • CI/CD pipelines and automated deployment processes • Git-based development workflows • AWS IAM, security, logging, monitoring, and cost management The successful candidate does not need to write production code every day, but must understand the technology well enough to challenge estimates, identify dependencies, recognize risks, communicate with senior engineers, and determine whether reported progress is credible. COMMUNICATION AND PROJECT-MANAGEMENT EXPECTATIONS We are looking for someone who communicates clearly, directly, and consistently. You must be able to: • Translate business and customer requirements into clear technical deliverables • Work with developers to confirm exactly what will be delivered and when • Break large objectives into manageable sprint tasks • Ensure every task has an owner, due date, definition of done, and measurable acceptance criteria • Ask detailed questions when requirements, estimates, or progress reports are unclear • Identify bottlenecks early and create a specific recovery plan • Communicate delays immediately rather than waiting until a deadline is missed • Provide concise written updates showing completed work, current work, risks, blockers, and next steps • Lead meetings efficiently and document decisions, action items, owners, and deadlines • Communicate professionally with technical teams, executives, clients, and AWS stakeholders REQUIRED QUALIFICATIONS • 5+ years of technical project or program management experience • Significant experience managing AWS cloud and DevOps projects • Experience managing large distributed engineering teams • Experience coordinating multiple projects and workstreams simultaneously • Strong understanding of Agile delivery and two-week sprint management • Experience using Jira, ClickUp, Asana, Monday.com, Azure DevOps, or similar tools • Excellent written and spoken English • Strong leadership, organization, documentation, and problem-solving skills • Ability to work with teams across multiple countries and time zones • Demonstrated ability to recover delayed or at-risk technical projects PREFERRED QUALIFICATIONS • AWS certification, such as AWS Certified Solutions Architect, DevOps Engineer, Machine Learning Engineer, or AI Practitioner • Experience delivering generative AI, RAG, or agentic AI solutions • Experience working for an AWS Partner • Experience with AWS-funded customer programs or AWS Marketplace solutions • Technical background in cloud engineering, software development, or DevOps • Experience managing teams located in the United States, India, Pakistan, or Bangladesh WHAT SUCCESS LOOKS LIKE • Two-week sprints begin with clearly defined and properly assigned work • Developers understand their deliverables, deadlines, and acceptance criteria • Leadership has an accurate, real-time understanding of every project • Problems and bottlenecks are identified early and addressed quickly • Commitments are based on validated engineering estimates • Deliverables are completed on schedule and meet client expectations • Project documentation and sprint boards remain accurate and current HOW TO APPLY Please provide: 1. A summary of your AWS, DevOps, and AI project-management experience 2. The largest engineering team you have directly managed 3. An example of how you managed multiple projects using two-week sprints 4. An example of a delayed or blocked project you helped recover 5. Your experience with Amazon Bedrock, AgentCore, SageMaker, RAG, or agentic AI 6. The project-management tools you use and how you track deliverables, dependencies, and blockers 7. Your AWS certifications, if applicable 8. Your working hours and time zone 9. A short video or Loom introduction explaining why you are a strong fit Please begin your proposal with the words “AWS DELIVERY” so we know you read the complete posting. This is expected to be a long-term engagement for the right person.

Principal / Staff AI Engineer — Agentic Systems, Memory & LLM Intelligence We are building an AI-native communication platform and are looking for an exceptional hands-on Principal/Staff-level AI engineer to work directly with our senior technical team on the intelligence layer of the product. This is not a traditional software engineering role. We already have strong frontend, backend, infrastructure and DevOps resources. We are specifically looking for someone who has deep experience building production agentic systems, LLM reasoning workflows, memory systems and highly personalized AI outputs. We do not want an AI strategist or someone who primarily manages other engineers. We want someone senior enough to architect the system, but technical enough to personally build it. What we’re building Our product understands how an individual communicates and uses that understanding to help manage communication across systems such as email, Slack and calendar. The challenge goes substantially beyond generating or rewriting text. We want the system to reason over: * Who is communicating * The history of the conversation * The relationship between the people * What has happened previously across channels * Relevant long-term and short-term memory * The user’s communication style and preferences * The counterparty’s communication style * The user’s likely objective * What action should happen next The system then needs to determine whether to draft, recommend, ask, retrieve additional information, use a tool or eventually take an action. We are trying to make the AI remarkably good at understanding the situation before it generates anything. What you will actually work on You will work directly with our senior technical team to design, prototype, test and ship the intelligence layer behind the product. This includes: * Designing agentic reasoning and execution workflows * Advanced context engineering — determining exactly what information a model should receive and when * Building systems that convert raw conversation/history into useful reasoning context * Improving prompting and reasoning strategies across complex communication scenarios * Designing short-term, long-term and relationship memory * Retrieval, relevance scoring, memory decay and context selection * Agent planning, routing, tool selection, retries and handoffs * Building reliable tool-calling across email, Slack, calendar and other systems * Determining where workflows should be deterministic versus agentic * Human-in-the-loop systems for draft → recommend → approve → act * Building rigorous eval frameworks for output quality and agent behavior * Model selection and experimentation across leading frontier models * Tracing, observability and debugging of agent decisions * Improving latency, reliability and cost without sacrificing intelligence * Writing production code and shipping alongside our existing engineering team The technical profile we’re looking for You should be genuinely strong in several of the following: Agentic AI * Production LLM agents * Multi-step reasoning and execution * Tool/function calling * Agent routing and orchestration * Planning and state management * Structured outputs * Failure recovery and retries Context & Memory * Context engineering * RAG and retrieval systems * Semantic search / embeddings * Long-term memory architectures * Episodic/user/relationship memory * Relevance ranking and context selection * Memory summarization, consolidation and decay LLM Engineering * Advanced prompting techniques * Model behavior and failure analysis * OpenAI/Anthropic/Gemini or equivalent frontier-model APIs * Model routing and selection * Structured generation * Streaming * Latency/cost optimization Agent Frameworks Experience with some combination of: OpenAI Agents SDK, LangGraph, LangChain, LlamaIndex, PydanticAI or equivalent orchestration systems. We care more about your understanding of the underlying architecture than your loyalty to any particular framework. Evaluation & Observability * LLM/agent evals * Regression testing * Golden datasets * LLM-as-judge approaches * Trace analysis * Prompt/model experimentation * Production observability Engineering * Excellent Python * Strong API/backend engineering * TypeScript familiarity is valuable * Production databases/vector stores * Async systems and queues * Ability to work inside an existing production architecture Who we DON’T want Please do not apply if your primary experience is: * Generic full-stack development with some OpenAI API calls * Basic chatbots * Simple RAG applications * Prompt writing without production engineering * AI consulting/strategy without personally building systems * No-code/low-code agent builders * Primarily managing AI teams rather than writing code We are looking for someone who can sit with senior engineers, debate architecture at a very high level, open the codebase immediately afterward and build it. Ideal background You have already built and shipped a meaningful AI-native product or production agent system. You have dealt with the ugly parts: hallucinations, bad retrieval, context pollution, nondeterministic workflows, tool failures, latency, prompt regressions, memory quality and agents that work beautifully in demos but fail in production. You understand that making an agent truly good requires experimentation, measurement and engineering—not simply changing prompts until something looks better. Engagement We expect this to begin as a high-intensity hands-on engagement working directly with our senior technical team. If the fit is exceptional, there is an opportunity for this to become a substantial ongoing role. We care far more about capability than geography. To apply Do not send us a generic AI proposal. Please answer these four questions: 1. What is the most sophisticated production agentic system you personally built? Describe the architecture and specifically what YOU built. 2. How did you handle context and long-term memory? What worked, what failed, and what would you do differently today? 3. How did you evaluate whether changes to prompts, models, retrieval or agent architecture actually improved the system? 4. Imagine an AI has access to a user’s email history, Slack conversations, calendar, communication profile and relationship history with another person. Before drafting an important reply, how would you determine what context the model should actually see? (Important question !) Please include GitHub, technical writing, architecture examples or production systems you’ve personally built if you can share them. If your answer to these questions is strong, we want to talk.

The Warehouse Co is a Denver AI consulting and workflow automation firm. We build AI that works in production, not demos. We're hiring one engineer to give a single client their full-time focus: a well-funded data center and energy infrastructure developer with a small, senior team that has decided to go AI-enabled end to end. THE STACK (Claude-first) - Claude for Enterprise, connected to Microsoft 365, Box, Slack and Notion - Claude Code and the Claude Agent SDK for custom agents - MCP as the integration layer - n8n, self-hosted, for orchestration - Rippling for people and finance WHAT YOU'LL BUILD IN THE FIRST 90 DAYS - Inbox triage agent across Outlook and Slack. It summarizes threads, pulls out commitments, and drafts replies that a human sends. - Document agent over Box. Pick the right NDA form, compare redlines, summarize leases and contracts, then turn data room intake into a diligence report with page-level citations. - Monitoring pipeline. Track legislation and regulation, deliver a digest, send Slack alerts. - Back office automations. Invoice intake and filing, a weekly payables forecast, an onboarding pipeline. - The foundation under all of it: connectors and permissions, a shared skills library, eval sets, and cost and audit reporting. HOW THE TEAM WORKS You own the build. Our founder owns architecture and the client relationship. Our product lead owns priorities and user feedback. You ship, measure and iterate daily, and you present your own work to the client's team. Expect to spend most of your time building and a real share of it in the client's Slack. HOW WE WORK Human approval on anything that sends, signs, pays or shares. Least privilege on every connector. Client data never trains a model. Every agent ships with an eval set and a cost line. Drafts and summaries cite their sources. "Saves time" is a number here, not a feeling. MUST HAVE - Production work with the Claude API and Claude Code: tool use, structured outputs, prompt caching, Agent Skills, hooks, subagents. - MCP servers or clients you built or deployed, with real authentication and credentials kept away from the model. - Expert n8n: self-hosted, AI Agent node, MCP nodes, retries and error branches. Equivalent depth in Make, Zapier or Power Automate counts if you're willing to standardize on n8n. - Working Python or TypeScript: REST APIs, webhooks, OAuth, JSON, Git. - Security instincts: least privilege, human approval gates, egress control, no client data used for training. - US-based, available 40 hours a week, with Mountain time overlap. STRONG PLUS Claude Agent SDK or Managed Agents in production. Claude Enterprise administration (SSO, SCIM, audit log export, retention policy). A published or internal MCP server with Streamable HTTP and OAuth. Agent Skills other people use. Copilot Studio or Power Automate inside an M365 tenant. Eval harnesses and observability. Durable execution (Inngest, Trigger.dev, Temporal). An Anthropic certification (CCDV or CCAR). Prior work with legal, real estate or energy documents. A track record of cutting a model bill with prompt caching, batching and model routing, with numbers to show. TERMS Hourly, up to $100/hour, 40 hours a week. 90-day initial term with the intent to extend to 12 months. Remote, US only. NDA and IP assignment signed before any client access; the client's confidentiality terms flow down to you. HIRING PROCESS Application with video and GitHub, then a 30-minute screen, a paid 3-hour build task, and a paid 2-hour pairing session on real backlog work. Two weeks from application to start for the right person. TO APPLY Answer the three screening questions, link your GitHub, and include a 2-3 minute video walking through the most complex Claude or LLM workflow you have shipped. Applications without the video are not reviewed. The client is unnamed and workflow descriptions are generic on purpose. The detailed roadmap goes to finalists under NDA.

We’re building Involve, a student engagement platform for schools that brings community discovery, events, student organizations, and school operations into one connected product. We’re preparing for an initial school pilot and are looking for an experienced full-stack product engineer to help turn an extensively designed and researched product into a production-ready V1. This is not an early-stage “I have an app idea” project, and we are not looking for someone simply to translate Figma screens into code. A substantial amount of product work has already been completed. We have high-fidelity Figma designs covering most of V1, an established design system and component library, defined user flows and information architecture, student/user research, documented V1 scope, roles and permissions, school/district organizational models, and detailed product/domain documentation. What we’re looking for We’re looking for a senior, product-minded full-stack engineer with meaningful startup experience, ideally someone who has worked as a founding engineer, early startup engineer, technical founder/CTO, or has personally helped take a SaaS product from zero-to-one. You should be comfortable working across the stack and making architectural decisions rather than waiting for perfectly specified engineering tickets. Our current technical direction includes: * React / TypeScript * Modern component-based frontend architecture * PostgreSQL / Supabase * Authentication and authorization * Row-Level Security and multi-tenant data isolation * REST/API integrations * OAuth 2.0 / OIDC / SSO * Google Workspace and/or Microsoft Entra integrations * Testing and CI/CD * Production deployment, logging and observability Experience building secure multi-tenant SaaS applications is particularly valuable. AI-native engineering This is especially important to us. We want someone who was already a strong software engineer before modern AI coding tools, but now uses tools such as Claude Code, Codex, Cursor, Copilot, or similar agents aggressively to increase engineering velocity. We are interested in using AI throughout planning, implementation, testing, debugging, documentation and code review. At the same time, we’re not looking for AI-generated “vibe coding.” The engineer must personally maintain responsibility for architecture, security, authorization, code quality, testing and maintainability. We’d love to hear how AI has changed the way you personally build production software. What you’ll be doing You’ll work directly with the founder/product designer to: * Review the existing V1 scope, designs and technical documentation * Help determine the simplest production architecture for the initial pilot * Identify unnecessary complexity and challenge requirements when appropriate * Build the application across frontend and backend * Translate the existing design system into reusable production components * Implement authentication, permissions and school/organization data boundaries * Build and integrate core product workflows * Implement external identity/directory integrations * Establish testing, deployment and development workflows * Document important architectural decisions * Ship frequently to staging so the product can be tested throughout development * Help prepare the application for a real school pilot We care much more about shipping a small, excellent V1 than building unnecessary infrastructure for hypothetical future scale. How we like to work We want to operate like a small startup product team, not a traditional client/agency relationship. The founder owns product strategy, UX research and primary product design. We are also considering working with an experienced senior product designer during implementation to help audit and refine the design system, usability, edge cases and developer handoff. We want engineering involved in product decisions early. We value direct communication, frequent collaboration, thoughtful disagreement and people who challenge assumptions rather than simply saying yes to requirements. Ideally, product, design and engineering can discuss a problem together, make a decision quickly, implement it, put it in front of users and iterate. We prefer frequent working releases over a large delivery at the end of the project. What success looks like The goal of this engagement is not simply to “finish the screens.” Success means we have a V1 that: * Accurately implements the core product experience * Is secure and properly isolates users/schools/organizations * Has clean, understandable and maintainable architecture * Has appropriate automated testing around critical workflows * Can be deployed and monitored reliably * Can be tested with real users throughout development * Is documented well enough that another strong engineer could enter the codebase without depending on the original developer * Is ready for an initial school pilot All production source code, repositories, infrastructure and related accounts will remain under Involve’s ownership/control. Who we’d especially like to meet We’d particularly like to hear from engineers who have experience as a: Founding Engineer · Technical Founder · Startup CTO · Early Startup Engineer · Senior/Staff Product Engineer Previous experience in education technology, regulated industries, identity/access management, enterprise SaaS or products handling sensitive user data is a plus. We’re open to beginning with a smaller paid engagement to establish fit before expanding into the complete V1 build. If we work exceptionally well together, there is potential for a much longer-term relationship as Involve grows.

**If you are associated with an agency, please do NOT apply.** We're looking for an experienced AI full-stack developer to spend approximately 2–3 hours auditing our existing AI SaaS platform and providing practical recommendations for improvement. The platform is already built, so we're not looking for someone to rebuild it. We want a strong engineer who can quickly understand the existing architecture, identify weaknesses and opportunities, and give us valuable technical and product-level ideas. U.S.-based freelancers only. Security is an important concern for this project, so strong experience with secure SaaS architecture, authentication, API security, and handling sensitive data is highly valuable. Required Skills - Strong Python development experience - Strong Next.js / React experience - AI/LLM application development - AI SaaS architecture - REST APIs and third-party integrations - Database architecture and optimization - Authentication and authorization - API and application security - Secure handling of user and application data - Cloud deployment and infrastructure experience - Ability to review an existing codebase quickly - Strong debugging and architectural analysis skills What You'll Do During the initial 2–3 hour engagement, you'll review the existing platform and focus on areas such as: - Overall application architecture - Python backend - Next.js frontend - AI/LLM implementation - API integrations - Authentication and authorization - Data flow and database structure - Security vulnerabilities and potential risks - Performance and scalability - Error handling and reliability - Code organization and maintainability - Opportunities to improve the AI experience - Potential product or workflow improvements We're especially interested in practical ideas that can improve the platform, not just a list of generic recommendations. Deliverables At the end of the audit, we'd like: - Key technical issues or risks you identify - Security concerns that should be addressed - Architecture improvement recommendations - AI/LLM improvement opportunities - Performance and scalability recommendations - Product/workflow ideas where you see clear opportunities - A prioritized list of recommended next steps A short written summary is sufficient. We don't need a lengthy formal report. What We're Looking For We're looking for someone who can think like a senior engineer and product-minded architect, not simply review code line by line. You should be able to quickly understand an unfamiliar AI SaaS application, ask the right questions, identify meaningful problems, and explain your recommendations clearly. Strong experience building and auditing production AI SaaS platforms with Python and Next.js is preferred. Please Include When applying, please briefly provide: 1. A few examples of AI SaaS or AI-powered applications you've built. 2. Your experience with Python and Next.js. 3. Your experience with AI/LLM applications. 4. Examples of security or architecture audits you've performed. 5. What areas you would prioritize during a 2–3 hour audit. 6. Your hourly rate and availability. 7. Confirmation that you are currently located in the United States. If the initial audit goes well, there may be an opportunity for additional development and ongoing technical work. We're looking for someone who can provide honest, practical, and high-value feedback and potentially become a long-term technical partner.

We are looking for a writer who can handle 1 piece of content per week for Food Processing Plants. These will include articles such as "What is CIP", "Wastewater Reclamation Systems" and "What Causes Cavitation in Pumps?" We need this content to be written in Surfer SEO with a score of 60 or better. Generally, pieces will be 800 to 1200 words. Looking to pay 8 cents a word. We are looking for human-written content and will run the content through Originality.ai and look for a score of 60% human or better. The Surfer SEO link will be provided, but we don't provide an originality subscription.

We’re looking for a staff-level software engineer for a focused consulting engagement to strengthen an existing AI system and establish the engineering foundation for scale. You’ll work directly with the research lead, combining hands-on implementation with architectural judgment and independent technical ownership. The goal is to make the software reliable and efficient today, with a clear path to supporting larger workloads, more complex agent workflows, and a growing development team. This includes simplifying the existing implementation, establishing sound architectural boundaries, and building the testing, evaluation, and operational practices needed for continued development. Relevant experience and qualifications: - Enterprise-scale systems. Experience taking technical ownership of complex production software from architecture and implementation through deployment and operation. - Foundational architecture. A track record of turning rapidly developed software into maintainable systems that can grow in usage, functionality, and contributors. - AI agents and orchestration. Hands-on experience with agent workflows, tool integrations, execution state, concurrency, and failure recovery. - Human-agent collaboration. Experience building interactive or real-time software where people work directly with agents, including shared context, progress visibility, intervention, and approval. - Evaluations and testing. Experience establishing evals and regression coverage that distinguish model-quality problems from application defects and support confident iteration. - Performance and reliability. Strong debugging and profiling skills, with experience managing latency, throughput, resource usage, and external-service dependencies. - Practical engineering judgment. Ability to determine what to repair, simplify, replace, or remove, and to choose abstractions appropriate to the system’s current needs and expected growth. - Python and operational tooling. Ability to work confidently in an existing Python codebase and establish useful observability, reproducible environments, automated checks, and deployment practices. - Research collaboration. Ability to translate research goals into concrete engineering priorities and communicate technical tradeoffs clearly. The engagement should leave behind implemented improvements, a coherent architecture, meaningful validation, and a clear technical handoff. Please include examples of scalable production systems, human-agent collaboration tools, or foundational engineering work you’ve delivered.

SANDBULL AI helps owners of trades and local service companies install the AI and automations that already work in their shop. We need a US-based contractor we can pull onto specific client projects as they close. Not a retainer seat. Not a full-time hire. When a project is live, you jump on it, ship it, and we close that ticket. What you will actually do Turn an audit into a build: follow-up sequences, booking, dispatch or estimate flow, inbox, CRM, simple internal tools Connect the stack the client already has (often GoHighLevel, plus whatever they run for jobs, phone, and payments) -Write the SOPs so the owner and their people can run it without us -Join short client calls when we need the implementer in the room -Flag what is out of scope before you start clicking This is not -A sales role -A “prompt engineer” or ChatGPT-wrapper gig -Agency account management - Building our marketing site Must be true -You live and work in the United States (we will only hire US) -You have shipped AI or automation inside real service businesses, not just SaaS demos -You can work from a written brief and a Loom, and you do not need daily standups -You can write clearly to a 50-year-old shop owner -You are fine with project billing (fixed per project or a capped hourly block, we decide per job) Nice if true GoHighLevel, Zapier or Make, and one of: AccuLynx, ServiceTitan, JobNimbus, Housecall Pro, or similar You have sat on a call with a contractor or home-services owner before How to apply Do not send a generic cover letter. In your proposal, take a loom/video and answer these three: 1. One AI project you actually shipped (what you installed, what changed) 2. The tools you used 3.Your hourly rate and a ballpark for a 2-week implementation 4. A goal you are currently working towards in your life. If those are missing, we will skip the proposal.

**Chemical Engineering Resume + LinkedIn Optimization for New-Grad Job Search** I am a senior Chemical Engineering student graduating in May 2027 and am preparing to begin a full-time engineering job search this fall. I am looking for an experienced resume writer/career professional who has specifically worked with **chemical engineers, process engineers, manufacturing engineers, water/wastewater engineers, or other technical engineering candidates**. I am **not** looking for a generic resume rewrite or an AI-generated resume. I already have substantial content and experience. I need someone who understands engineering recruiting and can help me determine what to emphasize, how to quantify and describe my work, and how to position my background competitively for entry-level engineering roles. ### My Background My experience includes: * Chemical Engineering B.S. candidate at the University of South Florida, graduating May 2027 * Approximately 3.3 cumulative GPA * Engineering internship with **Seven Seas Water Group**, focused on desalination and water-treatment engineering * Experience with brackish-water reverse osmosis (BWRO), engineering design basis work, PFDs/process documentation, equipment/vendor evaluations, and engineering project workflows * Development of engineering workflow/technical bid evaluation improvements during my internship * Undergraduate chemical engineering research involving catalysis, dry methane reforming, photocatalysis, catalyst preparation, experimental testing, GC/data analysis, and reaction engineering * Aspen Plus process simulation experience * Water desalination process simulation and optimization project * Rigorous distillation/process-design project involving Aspen Plus and tray-by-tray column design * Previous experience in data analysis, a regulated pharmacy environment, and team leadership/operations ### Target Positions I expect to primarily apply to: * Process Engineer / Process Engineer I * Chemical Engineer * Project Engineer * Water / Wastewater Process Engineer * Water Treatment / Desalination Engineer * Manufacturing Engineer * Production / Operations Engineer * Environmental Process Engineer * Applications Engineer * Process Development Engineer * Entry-level R&D / technical engineering roles My primary geographic target is the **greater Tampa Bay, Florida area**, although I may also apply more broadly throughout Florida and the Southeast. ### What I Need **1. Resume Optimization** I want a strong, ATS-compatible, one-page engineering resume designed for entry-level/new-graduate recruiting. I would like help with: * Determining which experiences deserve the most space * Writing technically accurate, accomplishment-focused bullets * Quantifying impact where appropriate * Positioning my Seven Seas internship as the centerpiece of my engineering experience * Properly presenting undergraduate research * Presenting Aspen Plus and engineering projects effectively * Deciding how much of my older non-engineering work experience to retain * Keyword/ATS optimization without keyword stuffing * Making the resume effective for both recruiters and engineering hiring managers Ideally, I would also like guidance on creating **2–3 targeted versions** of the resume, such as: * Water / Process / EPC Engineering * General Process / Manufacturing Engineering * R&D / Process Development **2. LinkedIn Optimization** I would also like assistance improving my LinkedIn profile for engineering recruiting, including: * Headline * About section * Experience descriptions * Skills * Internship presentation * Research presentation * Keyword optimization for recruiter searches ### Who I Am Looking For Strong preference will be given to someone who has: * Experience writing resumes for **Chemical Engineers or closely related engineering disciplines** * Experience with new graduates / engineers with 0–3 years of experience * Knowledge of process engineering, manufacturing, EPC, water/wastewater, chemical manufacturing, pharma, energy, or similar industries * Familiarity with ATS systems and modern engineering recruiting * Experience optimizing LinkedIn profiles for engineering candidates A technical or engineering background yourself would be a major plus. ### Please Include With Your Proposal Please answer the following: 1. What experience do you have specifically with Chemical Engineering, process engineering, manufacturing engineering, water/wastewater engineering, or similar technical resumes? 2. Have you worked with new engineering graduates or candidates with less than 2 years of professional engineering experience? 3. Please provide 1–2 anonymized examples of engineering resumes you have worked on, if possible. 4. How would you approach positioning a candidate with a 3.3 GPA but strong internship, undergraduate research, and technical-project experience? 5. Do you personally perform the resume work, or is any portion outsourced or primarily AI-generated? 6. Does your service include revisions? 7. Can you also optimize my LinkedIn profile? **Please do not send a generic proposal.** I will prioritize applicants who demonstrate that they understand engineering recruiting and the technical nature of my background.

## Project Overview We're building an iOS dating app for the LGBTQ+ community. The frontend (SwiftUI) and backend (Supabase) are largely complete, and we're looking for a highly experienced iOS engineer to conduct a full technical audit, close any gaps, and bring the app to production-ready status. We need someone who works fast — quick to spot issues, quick to apply fixes, and quick to execute — without cutting corners on quality. ## Timeline This is a fast-moving engagement. We're looking to complete the full scope of work within 6–10 weeks, with 1–2 weekly touchpoint calls (45–60 min each) for progress updates and alignment. --- ## Scope of Work Audit & Fixes - Audit the existing SwiftUI frontend and Supabase backend for code quality, architecture, and security - Quickly identify gaps, bugs, and vulnerabilities, and document findings - Fix identified issues and implement necessary improvements with minimal turnaround time Scale & Architecture - Architect/optimize for scale — the app needs to comfortably support 200,000+ monthly active users without requiring future rework - Architect for a phased, city-by-city market rollout, allowing new markets to be added incrementally without major rework to the codebase Feature Integration - Integrate AI-powered features such as profile suggestions, reply/message suggestions, and other AI-driven enhancements valuable to a dating app experience - Integrate payment systems through the App Store (In-App Purchase/StoreKit) and other platforms as needed (e.g., Polar) - Integrate analytics (e.g., PostHog), authentication, and application monitoring/error-tracking tools (e.g., Sentry) Safety, Compliance & Launch - Implement photo/image scanning and content moderation systems as required by App Store guidelines and applicable law (e.g., CSAM detection, inappropriate content screening) - Recommend and implement best practices aligned with current industry standards for security, safety, and data privacy (including CA and U.S. requirements such as CCPA/CPRA) - Prepare the app for App Store submission and manage the submission process through to approval --- ## What We're Looking For Core Technical Expertise - Highly experienced, senior-level iOS engineer with a proven track record shipping and scaling production apps in SwiftUI - Strong hands-on experience with Supabase (or comparable Postgres/BaaS backends) - Experience designing for high-scale, high-growth user bases - Fast, sharp, and detail-oriented — able to quickly spot issues and apply effective fixes without extensive back-and-forth Domain Experience (Required) - Has worked on more than one social networking or dating app and successfully brought at least one to a completed, production-ready launch - Must have strong experience in location-based dating apps with maps, grids, etc. - Proven, successful experience submitting apps to the App Store and navigating App Review to approval - Experience implementing photo/image scanning and moderation systems as mandated by App Store guidelines and relevant law (e.g., CSAM detection, content safety pipelines), particularly for apps with user-generated content - Experience architecting apps for phased geographic rollouts (e.g., launching one city at a time and expanding without major rework) - Experience integrating AI features such as profile suggestions, smart reply/message suggestions, or similar AI-driven functionality - Experience integrating payment systems, including App Store In-App Purchases/StoreKit and third-party platforms (e.g., Polar) - Experience integrating analytics platforms (e.g., PostHog), authentication systems, and application monitoring/error-tracking tools (e.g., Sentry) Compliance & Privacy - Deep understanding of user data privacy requirements in the U.S. and California (e.g., CCPA/CPRA) - Experience with EU data privacy (GDPR) is a bonus Logistics - Based in and physically located within the United States (no exceptions — work cannot be outsourced or performed outside the U.S.) - Comfortable signing an NDA and IP Assignment Agreement before project details are shared and before work begins - Available for 1–2 weekly touchpoint calls (45–60 min each) throughout the engagement