# Full-Stack AI Engineer — Semantic Search + Next.js + Supabase (Long-Term, Contract-to-Hire) ## About We're building an AI-native platform that makes a large archive of recorded talks genuinely discoverable and useful: need-based semantic search over transcribed media, with a subscription product built around it. We have a clear product vision and architecture and are looking for a lead engineer to build the first version and grow with us long-term. Full product details are shared with shortlisted candidates under NDA — this post focuses on the engineering and the skills we need. ## The engineering challenge You'll build a two-part system that shares one database: 1. **A content pipeline (Python):** ingest recorded talks, transcribe them, chunk and enrich the transcripts with metadata using an LLM API, generate embeddings, and store everything in Postgres. 2. **A web app (Next.js):** fast, crawler-friendly, SEO-strong content pages with structured data; retrieval-based search that returns relevant source material with links/citations; user accounts; and Stripe-gated paid content. We care a lot about retrieval *quality* and clean, maintainable architecture — this is a real product, not a prototype. ## Required tech stack - **App:** Next.js (App Router), TypeScript, Vercel. Strong SSR/SSG, SEO, and JSON-LD structured-data experience. - **AI/backend:** Python; production RAG (embeddings, chunking, retrieval quality); LLM API integration. - **Data:** Postgres + **pgvector** (via Supabase); embeddings via a hosted model (Voyage/OpenAI). - **Auth & gating:** Supabase Auth with row-level security. - **Payments:** Stripe (subscriptions + one-time). ## Required skills - Shipped production Next.js (App Router) + TypeScript apps with strong SSR/SEO. - Built a real RAG / vector-search system in production — not a tutorial clone. - Comfortable in Python for data pipelines. - Postgres + pgvector and Supabase in production. - Stripe integration. - Plans before building; communicates clearly in writing. ## Nice to have - Audio/video transcription experience (Whisper / faster-whisper / Deepgram / AssemblyAI). - Agentic coding workflows (e.g., Claude Code). - Content-heavy SEO products or media libraries. ## Engagement - Hourly, contract-to-hire. ~20–40 hrs/week to start; long-term for the right person. - We start finalists on a **small paid test project** (a single self-contained slice of the pipeline) before the full engagement — that's how we evaluate fit. ## Confidentiality This is a proprietary product. Shortlisted candidates sign a mutual NDA before we share full scope and context. Please don't expect complete product details in the first exchange — strong technical applicants will have everything they need to be evaluated, and the rest follows the NDA. ## How to apply Applications that skip these are ignored: 1. **Start your proposal with the word `pgvector`** so we know you read this. 2. Link **two** projects: one live Next.js/SSR app, and one RAG/embeddings or LLM-integration project. Tell us what *you* personally built. 3. Answer briefly: *An offline embedding pipeline and a live search query must use the same embedding model — why does that matter, and how would you guarantee it?* 4. One line on your approach to chunking long-form audio/video transcripts for good retrieval.

We are looking for an experienced AI automation developer to build a private executive assistant named Jarvis for a business owner named Vince. Jarvis must operate as a professional, respectful, fast-moving executive assistant. The assistant will communicate with Vince through iMessage, access his Google Calendar, remember important information, send meeting reminders, and maintain local files/data on an office iMac. This is not a basic chatbot. We need a working AI assistant that can hold real conversations, remember context, anticipate needs, and protect Vince’s time. Core Requirements The assistant must: Communicate with Vince through iMessage on macOS. Store all data, memory, and files locally on the office iMac. Access Vince’s personal Google Calendar. Send Vince a message 20 minutes before meetings. Remember meeting times, preferences, important facts, and prior conversations. Use context from previous messages and stored memory. Start conversations professionally with: “Hello Sir. What do you need today sir.” Maintain a direct, respectful, professional tone. Avoid fluff, long explanations, repetition, and unnecessary questions. Understand that Vince has zero tolerance for wasted time. Validate Vince’s instructions and respond with useful answers quickly. Ask onboarding questions at first launch to learn Vince’s occupation, goals, priorities, communication preferences, daily routines, and assistant expectations. Be built in a way that can expand later into email, task management, document handling, and proactive reminders. Important Personality / Communication Rules Jarvis must be designed around Vince’s communication style: Direct. No fluff. No jargon. Lead with the answer. Never ask for information Vince has already provided. Protect his time, brand, relationships, and workflow. Jarvis should function as an executive personal assistant whose purpose is to remember everything so Vince does not have to repeat himself. Technical Scope The developer should be comfortable with: macOS automation. iMessage / Messages.app integration. Google Calendar API. Local file storage and local memory architecture. AI agent frameworks. Cron jobs or scheduler-based reminders. Secure credential handling. Local database or file-based memory. Python, Node.js, or similar automation stack. Optional: BlueBubbles, AppleScript, Shortcuts, SQLite, vector database, local LLM tools, OpenAI API, Claude API, or similar. There is already a macOS/iMessage path available using CLI-based message tooling, but we are open to the developer recommending the best reliable implementation. Existing iMessage automation concepts include sending, reading, and watching message history through macOS Messages.app tooling. Deliverables We need the developer to provide: Working Jarvis assistant installed on the office iMac. iMessage communication with Vince. Google Calendar integration. Automatic 20-minute meeting reminders by text. Local memory system. Local file/data storage structure. First-run onboarding question flow. Prompt/personality system for Jarvis. Basic admin documentation showing how to restart, update, and maintain the assistant. Security notes for credentials, permissions, and local storage. Testing checklist proving iMessage, memory, reminders, and calendar sync work. First-Run Intro Flow Jarvis should text Vince an introductory message and ask important setup questions such as: What is your primary occupation? What are your top business priorities right now? What meetings or events should I always remind you about? Who are your key contacts? What should I never interrupt you for? What should I always notify you about? What tone do you prefer from me? What daily reminders would make your life easier? What are your current goals for the next 30, 60, and 90 days? Ideal Candidate The ideal freelancer has built AI agents, personal assistants, calendar bots, local automation tools, or macOS/iMessage workflows before. We want someone practical who can build a reliable working system, not just create a demo. Please include: Similar AI assistant or automation projects you have built. Your recommended tech stack. How you would connect iMessage. How you would handle local memory. How you would secure calendar credentials. Estimated timeline. What you need from us to start.

We are building a semiconductor manufacturing intelligence platform designed to help engineers rapidly identify yield excursions, investigate root causes, and capture institutional process knowledge. A working foundation already exists, including yield dashboards, lot tracking, process-route visualization, maintenance-event correlation, and investigation timelines. We are now looking for a highly capable developer to extend and refine the system into a production-grade engineering decision-support tool. This is not a basic dashboard project. The goal is to enhance an existing platform into a system that connects manufacturing data, equipment history, and engineering knowledge with lightweight AI-assisted analysis. Key Objectives Help engineers answer questions such as: * Why did yield drop? * What changed before the excursion started? * Which tools or chambers are most likely responsible? * Have we seen a similar issue before? * What corrective actions worked previously? Scope of Work Investigation Workspace * Improve investigation timelines * Correlate process events, SPC/FDC signals, maintenance activity, and yield changes * Enhance interactive debugging workflow Historical Excursion Search * Simple similarity matching using rules or embeddings/API-based methods * Retrieve past investigations and outcomes Engineering Knowledge Layer * Searchable notes, documents, and reports * Store corrective actions and process changes AI-Assisted Summaries (lightweight) * Generate investigation summaries using an LLM API * Suggest possible contributing factors based on available data Ideal Candidate * Strong full-stack or data engineering experience * Comfortable working with existing codebases * Experience with analytics dashboards or industrial systems * Familiarity with APIs, databases, and data modeling * Bonus: exposure to manufacturing or semiconductor data Notes * This is an extension of an existing platform, not a rebuild * Focus is on practical implementation rather than complex architecture * Speed and execution matter more than theoretical design * Potential for ongoing work if collaboration goes well

Eligibility: This role is open to U.S. citizens only due to client security and compliance requirements. Please apply through this posting only — do not contact Data-Sleek directly regarding this position. Applications received outside this channel will not be considered and reported to Upwork. Data-Sleek is looking for a Senior AI Solutions Engineer to lead our on-premise and government-cloud AI deployments. You will design, build, and deploy AI-powered data pipelines for clients who cannot use commercial cloud due to ITAR, CMMC, or other data residency constraints, beginning with a client in the aerospace and defense sector. Beyond this first engagement, you will become Data-Sleek's go-to engineer for AI deployments across defense and aerospace clients, building the practice rather than just executing a single project. About Data-Sleek Founded in 2020, Data‑Sleek® is a U.S.-based AI and data consulting firm that helps mid-market companies build the data foundation that AI actually runs on. We own the full path — data strategy, architecture, integration, warehousing, and AI implementation — so organizations can adopt AI with confidence, stay compliant, and scale, without first hiring an internal data team. Our distributed U.S. team (San Francisco, Los Angeles, Irvine, Dallas, Chicago, and New York) partners with clients across healthcare, finance, insurance, logistics, and technology, modernizing data platforms with best-in-class tools like Snowflake, dbt, Fivetran, Tableau, and AWS. Trusted by Fortune 500 institutions and growing companies alike, Data‑Sleek turns complex data into measurable outcomes — faster insight, lower cost, and AI projects that deliver. About the Role You will own the technical delivery of AI-powered data pipelines in restricted environments where commercial cloud is not an option. The immediate engagement centers on a Product Lifecycle Management (PLM) data migration: building a pipeline that connects to a client's SharePoint on a restricted Microsoft 365 government tenant, reads engineering documents, classifies and summarizes them, detects duplicates, and rates naming-convention compliance to produce a migration-readiness report. You will start on-premise, then help the client evaluate and move to government cloud for production. Key Responsibilities AI Pipeline Development Build AI pipelines that connect to a client's SharePoint on a government cloud tenant, read engineering documents, classify them by type, generate summaries, detect duplicates, and rate naming-convention compliance in support of PLM data migration. Catalog large document repositories and produce migration-readiness reports and Excel catalogs that give clients a clear, measurable picture of their data. Engineer document-parsing workflows across DOCX, PDF, and XLSX formats, including embedding generation and database operations. On-Premise & Government Cloud Deployment Deploy on-premise first — a Mac Mini running Gemma via Ollama — standing up, serving, and tuning local inference infrastructure. Evaluate and migrate to production on Azure OpenAI (Azure Government) or AWS Bedrock (GovCloud) when the client is ready to scale. Keep deployments compliant within ITAR-sensitive, restricted-network boundaries throughout. Architecture & Cost Advisory Produce cost models and architecture recommendations that help client IT teams make informed platform decisions based on measured data, not vendor pitches. Compare deployment options — local, Azure Government, and AWS GovCloud — on cost, performance, and compliance, and explain the trade-offs clearly. Practice Building & Delivery Serve as Data-Sleek's go-to engineer for AI deployments across defense and aerospace clients. Build a reusable capability — a repeatable AI-solutions practice — rather than executing a single one-off project. What You Bring Required U.S. Citizen: U.S. citizenship is required and non-negotiable due to ITAR and client security and compliance requirements. Production LLM deployment: You have stood up inference infrastructure — not just called an API. You've handled model loading, memory constraints, failure modes, and throughput tuning in a real deployment. Local inference: Ollama, vLLM, llama.cpp, LM Studio, or TGI. You've served open-source models (Gemma, Llama, Mistral) on local hardware. Cloud AI platforms: Azure OpenAI or AWS Bedrock — at least one. Service configuration, model access, authentication, and token-based pricing. Python: Pipeline engineering — document parsing (DOCX, PDF, XLSX), API integrations, embedding generation, and database operations (SQLite, Postgres). Experience: 5+ years post-degree in software engineering, data engineering, or ML engineering. Strong Preferences Microsoft ecosystem: Entra ID, Microsoft Graph API, and SharePoint REST API at the API level. GCC High experience is a bonus. MCP (Model Context Protocol): Experience building or consuming MCP servers — a significant plus for a fast-evolving protocol. Workflow orchestration: n8n, Temporal, Airflow, or similar. The pipeline is orchestrated, not scripted. Government cloud awareness: Understanding of what FedRAMP High, IL4/IL5, and ITAR mean for cloud architecture decisions. Embeddings & vector similarity: sentence-transformers, pgvector, Qdrant, or FAISS for duplicate detection. 
Bonus (valued if present) Aerospace or defense experience: Familiarity with ECOs, BOMs, and AS9100 saves ramp time. Apple Silicon optimization: MLX, Metal acceleration, and Ollama tuning on M-series chips. Agentic frameworks: Bedrock AgentCore or Azure AI Foundry — the future direction involves agentic AI workflows on government cloud. What This Role Is Not Model training or fine-tuning. This is deployment engineering, not research. Data science or statistical modeling. The AI here is document understanding and classification, not predictive analytics. Frontend development. The deliverable is an Excel catalog and a report, not a web app. Sales or client acquisition. Data-Sleek's leadership manages the client relationship; you focus on delivery. Engagement & Compensation Remote, US-based. Occasional on-site travel to client facilities for hardware deployment and workshops may be needed. An average of 2–3 trips for the first engagement may be possible. Compensation. $40-$55/hour Why Join Data-Sleek? At Data-Sleek, you'll lead AI deployments in environments most engineers never touch — government cloud and on-premise systems where commercial tools simply aren't an option. Your work will directly shape how defense and aerospace clients adopt AI, and you'll build a reusable capability the company grows around. We focus on doing the right thing architecturally rather than selling the most expensive option, and we give our engineers the autonomy to deliver real solutions for real constraints. How to Apply If you've shipped real LLM deployments with real constraints, we'd like to hear from you. Please submit: Your resume A brief note describing one LLM deployment you've shipped — what model, what infrastructure, what data source, and what went wrong. Data-Sleek® is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all contractors.

Project Capital Advisors (PCA) is seeking an experienced AI Solutions Architect to support a government modernization consulting engagement. The initial project involves helping a county government modernize its citizen telephone operations using Voice AI, workflow automation, and enterprise system integrations. The selected consultant will work directly with executive leadership and public sector stakeholders to assess existing operations, develop technical recommendations, and help define an implementation roadmap. Responsibilities include: * Leading technical discovery sessions with client stakeholders * Evaluating existing phone systems and call workflows * Designing Voice AI and Contact Center AI solution architectures * Recommending enterprise platforms and integration strategies * Creating technical documentation and architecture diagrams * Supporting project scoping, budgeting, and executive presentations Preferred experience: * Enterprise Voice AI or Contact Center AI * Twilio, Amazon Connect, Genesys Cloud, Five9, Retell AI, ElevenLabs, Azure AI, or OpenAI APIs * API integrations and workflow automation * Experience with government or other regulated industries is preferred Engagement Details: * Contract/Fractional * Approximately 10–20 hours per week initially * Remote with occasional travel * Opportunity for additional engagements as PCA expands its Government AI consulting practice Please review the attached position description for complete responsibilities, qualifications, and application requirements.

I'm a retired entrepreneur and active investor looking for a skilled Claude AI practitioner to serve as a private tutor and advisor. I use Claude regularly and have a working Microsoft 365 integration in place, but I want an experienced guide to help me unlock advanced capabilities and build efficient, reusable workflows tailored to my work. This is not a beginner engagement. I learn quickly, prefer direct feedback over hand-holding, and want sessions focused on my actual use cases — not generic training. Topics to Cover - Claude Projects — structure and strategy for ongoing, organized work - Investment and general research — synthesizing company, market, and topic information efficiently - Correspondence — drafting polished emails in Outlook that match my voice with minimal editing - Document analysis — extracting key information from legal, financial, and fund documents - Microsoft 365 add-ins — what's available and genuinely useful for Word, Excel, and PowerPoint - Voice input and dictation — getting started and optimizing as a primary input method - Workflow building — creating persistent, reusable tools rather than starting from scratch each session - Agents, skills, and connected tools — connecting external tools, leveraging agentic capabilities, and building autonomous workflows - Prompt craft — advanced techniques applicable across all of the above Ideal Candidate - Hands-on experience with Claude (not just ChatGPT or general AI) - Background working with business operators, investors, or executives — not primarily developers or academics - Can demonstrate real-world applications, not just theoretical knowledge - Comfortable moving at a fast pace and adapting sessions to my priorities Format Virtual sessions via video call, 60–90 minutes each. Frequency to be determined based on fit and progress. Looking to begin with 4–6 sessions and reassess. To Apply Please include: 1. A brief description of your hands-on experience with Claude specifically 2. One or two examples of business or executive use cases you've worked on 3. Your availability and hourly rate A short introductory call before committing to paid sessions is expected.

I’m looking for a senior AI app developer who can help me build an AI-powered MVP while also guiding me through the technical decisions. This is not just a coding task. I want someone who can think through the product, recommend the right architecture, explain tradeoffs, and build the first working version. The ideal person should be comfortable with OpenAI/LLM integrations, full-stack development, database design, authentication, deployment, and startup-style MVP execution. I’d like to work with someone who can act almost like a technical partner: build the product, teach me what is being done, and help me understand how to maintain or scale it later.

AI Automation Engineer – Personal Injury Law Firm (Phase 1) Overview We are a plaintiff personal injury law firm seeking an experienced AI automation engineer to build a practical AI-powered operations system that reduces administrative workload and improves case management. Firm Profile: - 1 Attorney - 1 Legal Assistant - Approximately 100 Active Cases - Approximately 25 Cases in Litigation We are not looking for chatbots, prompt engineering, or marketing automation. We are seeking a builder who can deploy production-grade workflow automation integrated with our existing systems. --- Existing Technology Stack - CASEpeer - Supio - Gmail (Google Workspace) - Google Calendar - Google Drive - Claude - ChatGPT - CoCounsel - PLAUD --- Phase 1 Objective Build a Daily Case Command Center and Follow-Up System. The goal is to ensure that every morning the attorney knows exactly which files require attention and why. --- Deliverable #1: Daily Case Command Brief Generate a daily briefing containing: Litigation Matters - Upcoming depositions - Discovery deadlines - Hearings - Expert deadlines - Outstanding litigation tasks Pre-Litigation Matters - Treatment complete but no demand - Missing medical records - Records requests outstanding more than 21 days - Demands pending more than 30 days - Settlement checks outstanding Communications - Unanswered client emails - Unanswered adjuster emails - Unanswered defense counsel emails - Communications requiring attorney attention Case Velocity - Stale files - Cases with no recent activity - Recommended next actions --- Deliverable #2: Gmail Intelligence The system should: - Monitor designated Gmail inboxes and labels - Identify case-related emails - Extract action items - Detect deadlines - Identify follow-up opportunities - Draft suggested responses --- Deliverable #3: Follow-Up Automation Generate draft communications for approval: - Medical records requests - Provider follow-ups - Adjuster follow-ups - Client status updates - Scheduling communications No communication should be sent automatically. Human approval is required before sending. --- Deliverable #4: Calendar & Reminder Automation Create and manage: - Follow-up reminders - Litigation reminders - Discovery reminders - Records request reminders - Suggested calendar events --- Deliverable #5: CASEpeer Integration Where supported by available integrations/APIs: - Create tasks - Create notes - Associate communications with matters - Maintain activity history --- Technical Requirements Required: - n8n - Python - OpenAI API - Anthropic API - Gmail API - Google Calendar API - Google Drive API - REST API integrations Strongly Preferred: - LangGraph - LangSmith - Google Workspace administration - Salesforce integrations - CASEpeer, Clio, or Filevine experience - Legal technology experience - HIPAA or regulated-data experience --- Security Requirements - Human approval before external communication - Activity logging - Error handling - Retry mechanisms - Secure credential management - Documentation of workflows --- What Success Looks Like The system should: - Reduce administrative workload - Improve follow-up consistency - Reduce stale files - Improve litigation oversight - Improve case visibility - Provide a daily prioritized action plan --- Application Requirements Please include: 1. Similar workflow automation projects you have completed. 2. Experience with n8n and AI workflow automation. 3. Experience integrating Gmail and Google Workspace. 4. Experience integrating CRM or case-management systems. 5. Proposed architecture for this project. 6. Estimated timeline. 7. Estimated fixed-fee budget. Begin your application with: CASECOMMAND Applications without this keyword will not be considered. --- Budget Expected Phase 1 Budget: $8,000–12,000 Preference will be given to candidates who can demonstrate production deployments rather than proof-of-concept or prompt-engineering projects.

Before you apply read this first.: This is not a standard contract. We are not looking for someone to build something and disappear. We are looking for a developer who genuinely cares about people experiencing homelessness and wants to be part of something that actually changes lives. If that is not you, this post is not for you. If it is keep reading. What Is Operation Iron Gate: Operation Iron Gate is a physical intake hub in Vallejo, California built specifically for the unsheltered. Any homeless person can walk through our door and receive help no matter what barriers they are carrying, no matter what their documentation looks like, no matter how many times they have been turned away before. The rule is simple: nobody gets denied. We call it Yes by Default. Instead of looking for reasons to say no, we look for the next valid move. Every single time.When someone walks in, a case gets opened immediately. Every barrier they are facing gets documented. A next step gets started before they leave the building. And the system tracks every single movement of that case every action, every referral, every handoff until they actually receive the resource they need. Not a phone number on a piece of paper. The actual resource. It sounds too good to be true. That is exactly why we built the technology to make it real. What You Are Building — Phase 1 Only: We are currently hiring for Phase 1 only. This is the core platform build. We are keeping it focused. Once the pilot launches in Vallejo and the system is proven in the real world, we will move into the AI layer and long-term development. But right now we need Phase 1 built right. The platform is called DSAS the Digital Steward Authorization System. It is the brain of Operation Iron Gate. Here is what Phase 1 covers: * Case creation engine when someone walks through the door, a case opens immediately. One screen, one submit. * Barrier capture system every barrier the person is facing gets documented and connected to a next action. * Yes by Default enforcement a denial without a legal reason code is blocked by the system itself at the database level. * Three-tier triage system urgent cases get a 2-hour movement deadline with auto-calculated countdown. * Steward escalation queue hard cases route instantly to senior reviewers who make binding decisions. * MDO Live View: real-time monitoring screen showing every active case, who owns it, where it is stuck, how long it has been sitting. * Automated alert engine fires when a case stalls, when a deadline is missed, when a partner does not respond. * Glass Dashboard public-facing transparency layer showing the community how the system is performing in real time. * All data feeds automatically from the system. No manual entry ever. * IGAC credential system when someone arrives without ID, a credential is issued immediately on Day 1 so the case can keep moving. * 7-role permission matrix enforced at the API level on every endpoint. * Append-only audit trail tamper-evidence logging. Nothing gets deleted or edited after it is written.. The full technical specification is already built: * Every database field is named. * Every logic rule is written. * Every automated alert is defined. * Every role permission is mapped. You are not figuring out what to build. You are building something that is completely designed and ready to be coded. Technical Requirements Required: * Strong backend / full-stack development 3+ years building complex platforms * Relational database expertise PostgreSQL or MySQL complex logic enforced at the database level * Role-based access control enforced at the API level * Automated alert engines time-based and condition-based triggers * Append-only audit logging with tamper evidence * Real-time data feeds * REST API development Strongly preferred: React or similar framework for front-end interfaces. Mobile-responsive design Access Points may operate from tablets and phones. Civic tech, social services, healthcare, or government platform experience. We are open to your stack recommendation. Tell us what you would use and why. Who We Are Looking For: We need someone who is technically strong. But more than that we need someone who reads about Operation Iron Gate and feels something. Someone who understands that every field in this database is a real person's life. Someone who will flag a problem in the spec before building it wrong. Someone who stays when things get hard. Someone who wants to be part of this mission not just compensated for it. If you are only here for the money, this is not the right fit. If you want to build something that matters we want to talk to you. Compensation: Read This Carefully: This is a fixed-price contract via Upwork with milestone-based payments. Here is exactly how it works and why we structured it this way. We are paying in four milestones. Every single payment is tied to something real being delivered and confirmed not just promised. * 25% on project start you have skin in the game from day one and so do we. * 25% when the database and core logic is confirmed complete confirmed by the developer handoff checklist, not by your word alone. * 25% when the full intake flow and MDO Live View is confirmed complete again, confirmed by the checklist. * 25% at final sign-off when every single item on every Tier 1 handoff checklist is confirmed complete Nobody gets paid until something real is delivered and confirmed. The checklist is the protection for both of us. You cannot say something is done and collect payment if the checklist says otherwise. That is not a lack of trust that is how professional projects get built correctly. There is one more thing we need in this contract and we want to be upfront about it. The contract will include a scope change clause. That means if we ask you to build something that was not in the original specification something we did not think of, something new that becomes a change order and gets priced separately. It does not get quietly absorbed into the fixed price and it does not become a source of resentment on either side. You build what is specified. Anything beyond that gets negotiated openly and fairly. Fixed rate. Milestone payments. Handoff checklist as the payment trigger. Scope change clause in the contract. That is how we protect Pathfinders for Hope's mission funding and make sure you get treated fairly too. Additional terms: * NDA required before any specifications are shared * Work-for-Hire Agreement required before development begins all code belongs to Pathfinders for Hope * Rate is negotiable based on experience To Apply: Submit your proposal with your answers to all eight questions, a description of your most relevant project, your recommended stack, your honest Phase 1 timeline estimate, and your rate. We are building the operating system that proves homelessness can be solved one city at a time. If that is the kind of work you have been waiting for, we want to hear from you.

The Mission: We are a boutique digital marketing agency pivoting to high-value AI-integrated marketing services. Your goal is to help us productize common marketing workflows into repeatable service packages using AI tools and models that we can sell to our SMB clients. Our focus is social media, SEO/AEO, email marketing, and web dev. Do not apply if your main specialty is lead generation or paid ads. The Tech Stack: You must be a power user of Claude (Projects/Artifacts/Skills), Gemini (Gems), and OpenAI (Custom GPTs). You must have expertise building AI agents to assist with streamlining marketing workflows. We aren't looking for a prompter; we are looking for an architect who understands how to build 'human-in-the-Loop' systems that help streamline marketing. Experience with no-code and low-code platforms such as Bubble.io, Make.com, and Retool.com are highly preferred. The Human Element: We value soft skills, responsiveness, listening, and collaboration. You will be helping us bridge the gap between technical AI capabilities and real-world marketing ROI.