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.

We are hiring an AI Engineer for a remote opportunity with our Airlines project. The ideal candidate should have hands-on experience building GenAI solutions, including RAG pipelines, vector embeddings, prompt engineering, MCP server development, and integrating multiple LLM providers. Experience working with AWS Neptune (Graph DB), OpenSearch (Vector Store), Redis, REST APIs, and SSE-based streaming services is required. Exposure to LangChain, MCPSharp, or ModelContextProtocol.SDK is a plus. If interested, please share your updated resume along with your total years of experience, years of GenAI experience, RAG experience, MCP/Agentic AI experience, current location, work authorization, and availability to start.

EroFlow Intelligence is an enterprise-grade, autonomous AI orchestration pipeline designed to mitigate global supply chain disruptions for aerospace manufacturing. Built using a multi-agent framework, the system automates the entire lifecycle of risk detection, impact analysis, and procurement mitigation without requiring human intervention for standard operational anomalies. The architecture coordinates three specialized, asynchronous AI agents that communicate via a centralized event bus to solve complex logistical bottlenecks in real-time. Core Agent Architecture & Workflow 1. The Sentinel Agent (Data Ingestion & Extraction) Role: Continuous Global Monitoring. Function: Utilizes advanced LLM-driven web scraping and unstructured data extraction to monitor global news feeds, geopolitical shifts, weather anomalies, and shipping port telemetry. Trigger: If it detects a disruption (e.g., a port strike or critical mineral shortage), it extracts key entities (materials affected, estimated delay times) and passes a structured JSON payload to the orchestration layer. 2. The Impact Assessment Agent (Predictive Modeling) Role: Deep Cross-Referencing & Analytics. Function: Upon receiving a trigger, this agent cross-references the disrupted material with the company’s internal ERP (Enterprise Resource Planning) database and current inventory levels. Output: It runs a predictive analysis to determine exactly which production lines will stall and calculates the financial risk, assigning a high/medium/low priority score to the event. 3. The Mitigation & Logistics Agent (Autonomous Execution) Role: Operational Resolution. Function: If the risk score exceeds a specific threshold, this agent is authorized to take action. It autonomously queries pre-vetted alternative suppliers via APIs, negotiates standard volume pricing based on historical contract data, drafts a comprehensive procurement proposal, and queues the purchase order for final human sign-off (or executes it automatically if under a certain dollar cap). Technical Stack (The Blueprint) Frameworks: LangGraph / CrewAI (for multi-agent state management and deterministic routing). Core Language: Python 3.11+ Data Layer: PostgreSQL (for ERP syncing) & Pinecone / Qdrant (Vector database for storing and querying supplier contract PDFs and historical compliance documentation). LLM Orchestration: OpenAI GPT-4o / Anthropic Claude 3.5 Sonnet utilized via structured outputs (Pydantic parsing) to ensure strict API data integrity. Hosting & DevOps: Containerized via Docker, orchestrated via Kubernetes, and deployed on AWS with asynchronous task queues managed by Celery and Redis. Quantifiable Business Results (The Hook) 92% Reduction in supply chain anomaly response time (from 48 hours down to 14 minutes). Automated Recovery: Successfully mitigated over 140 potential production line stalls autonomously in simulated stress tests. Cost Efficiency: Saved an estimated $1.2M in expedited shipping fees by predicting bottlenecks 10 days before they impacted manufacturing floors.

Join our team as a senior AI Architect working closely with our product and engineer teams to design practical AI capabilities within our SaaS platform. This is a hands-on role focused on building reliable, production-grade conversational and AI-assisted features — not experimental research projects. You will work closely with product and engineering teams to design scalable AI patterns, integrate modern LLM technologies, and help shape how AI capabilities are embedded into real operational workflows. You will focus deeply on architecture, implementation quality, reliability, usability, scalability, observability, and operational robustness. This role is ideal for someone who understands both modern AI tooling and the realities of shipping enterprise SaaS software in production environments. We value people who can think critically about architecture, tradeoffs, operational realities, and long-term maintainability — not just prototype AI demos.

ABOUT THE PROJECTS. I run a curated marketplace on Shopify (The Ever Good) and I am building out an AI automation system to handle routine tasks in the business. I am AI-savvy and have built a Python agent myself, so I understand what I am asking for. I have the architecture planned and the business requirements semi-documented for each piece. I simply do not have the time to build everything I want to build, which is why I need a developer to work alongside me. The first project is a product review collection and import system. If it goes well, there is ongoing work building out additional agents over time, one at a time. This is a potential long-term engagement depending on how the first project goes. WHAT WE ARE BUILDING. The overall system is a set of AI agents that handle specific business tasks and route outputs to a simple dashboard where I can review, approve, and trigger actions before anything goes live. The dashboard is a Lovable build that gives me a single place to monitor agent status, review outputs, and approve or return anything that needs a human decision before it moves forward. Agents are planned across several areas of the business. We will build one at a time. I will walk you through the requirements for each before you start. Examples of agents here by business category (these could change): - MARKETPLACE & CATALOG. Product Reviews (first project), Catalog Enrichment and SEO, Pricing and Margin Monitor, New Product Auto-Pricer, Maker Stories, Maker Audit, Maker Analytics, Review Monitor. - CONTENT & SOCIAL. Content Generation, Cultural Moment Monitor, Social Publishing, Pinterest Curator, Instagram DM Automation, Image Production, Founder Content Amplifier. - MARKETING & ADS. Email Sequences, Paid Media Director, Ads Performance Monitor, LinkedIn Outreach. - SEO and Search. SEO and AI Search Visibility, Crawl Error and Redirects. - THE SCHOOL (Our Coaching Offerings). AI Readiness Assessment, Coaching Prep Tool, Workshop Launcher, Course Completion Monitor, Immersion Round Tracker. - FINANCE & OPS. Profit Police, Cash Flow Forecaster, Financial Health Monitor, System Health Monitor. - CUSTOMER SERVICE & GIFTING. Customer Service Drafts, Gift Inquiry and Proposals, Basket Assembler. OUR TECH STACK (Could Change). - AI AGENT BUILDING STACK. Claude Code, Claude API, Python, Make.com, Replit, Lovable, Google Sheets API, Baserow. - OTHER BUSINESS SYSTEMS. Shopify API, DropCommerce API, Matrixify, Typeform, Stripe API, Yotpo API, Klaviyo API, Instagram Business API, Later API, Pinterest API, ManyChat API, Bannerbear API, LinkedIn API, Google Analytics 4 API, Google Search Console API, DataForSEO API, QuickBooks Online API, Google Drive API. HOW WE WORK. - HOURLY. Collaborative and iterative engagement. - REQUIREMENTS. We review together before each build and refine as we go. - QA. I handle testing and QA on my end. - DOCUMENTATION. All work is documented throughout with decision logs and handoff notes so full ownership and control remains with me at every stage. - IP. All intellectual property and work product belongs to me entirely. - CLAUDE. You are expected to maintain your own Claude environment at a level that supports serious development work with no usage limitations. - ACCESS. All business systems and APIs provided with scoped credentials as needed. - DEPLOYMENT. Production deployment is handled collaboratively. - COMMITMENT. Looking for someone who can help maintain and evolve these tools over time while I retain full control and understanding of everything we build. WHAT I AM LOOKING FOR. - CLAUDE. Must use Claude as your central AI LLM. Experience. Working experience with the Claude API and the tools in the building stack above, or a demonstrated ability to learn new tools quickly. - COMMUNICATION.Clear communicator who works independently and does not need to be managed through a task, but is promptly responsive to me. - MINDSET. Building with AI tools as a regular part of your work. TO APPLY. Please share a brief description of one or two AI agents you have built, what they did, and what tools you used. Include a note on your familiarity with the tools listed above and your hourly rate.

Senior AI/ML Engineer / Claude architect — Legal Tech FirmProfit AI is the operational backbone of the modern law firm. We automate law firm operations end to end, and we're looking for a top-tier AI/ML engineer to help us build the next major platform in legal tech. We need a true expert. Someone deeply proficient with Claude and modern LLM architecture who has shipped real products at a high level. You're fluent across the full stack with Node.js, React, Postgres, MongoDB etc... and you have hands-on experience building with LangChain, LangGraph, MCP, and AWS Bedrock. We're not looking for someone who's read about LLMs. We're looking for someone who has shipped agents, orchestration layers, and production AI systems that real users depend on every day. Our current team is 8 engineers, we have firms signed and live, and we're moving fast. This is a chance to come in early, and have your work in the hands of customers within weeks. Contract to start, with a long-term path for the right person. Reply with the most impressive AI product you've shipped.

We're building an internal PPC keyword research and automation system to support paid acquisition across two brands (legal finance / legal tech). This is a one-time setup project — fixed scope, defined deliverables. The goal is a repeatable keyword research workflow, the automation pipeline behind it, and an AI-assisted review layer for QA and recommendations on what we pull. This is NOT ongoing campaign management or attribution work — strictly system build-out and handoff. WHAT WE NEED BUILT Keyword research workflow combining SEMrush, SpyFu, SimilarWeb, and Google Keyword Planner into a single repeatable process Automation pipeline that pulls keyword and competitor data on a recurring basis, normalizes it, and stages it for review AI-assisted review layer that evaluates new keyword sets — flags relevance, suggests groupings, surfaces gaps and overlaps with current campaigns Output formats: cleaned, structured keyword lists ready for Google Ads import, plus a decision log we can audit Documentation so the system is hand-offable to our internal team REQUIRED SKILLS Strong PPC and keyword research experience (Google Ads, SEMrush, SpyFu, or comparable) Automation and scripting (Python, Node.js, or similar) for pulling and normalizing keyword data Comfortable wiring up LLM APIs (OpenAI, Anthropic, or similar) for AI review steps Familiarity with Google Ads structure (campaigns, ad groups, match types, negatives) Clean documentation habits — system should be operable without you NICE TO HAVE Legal, fintech, or B2B/B2C hybrid vertical experience Direct Google Ads API integration experience Past work on internal marketing tools or admin dashboards SCOPE & LOGISTICS One-time project, fixed scope Deliverable: working system + documentation, not ongoing optimization Hourly engagement, completable in a few weeks Async-friendly — communication via Upwork messaging with occasional video sync

We are looking for 2-3 AI engineers/developers to help us build/complete an AI go-to-market (GTM) tool that looks at both structured and unstructured data, runs it all through our AI engine, and provides insights and recommendations straight to sales people. The tool is able to handle large volumes of data and provide actionable insights for business decision-making. Our engine consists of a prioritization algorithm, pattern matching and sentiment analysis. We use our recommendation engine to deliver the output (insights) straight to our tool which is "Apple-simple", intuitive and gamified. No more sales time wasted on looking at dashboards and trying to agree on which insights are important and which should be acted on. What you'll do Own features end-to-end across theFastAPI backend (IEngine) and Next.js 15 / React 19 frontend— from Claude prompt design to UI polish. Extend the intelligence pipeline: meeting ingestion (Google Drive + Deepgram realtime), Claude-driven action card generation, Neo4j relationship graph, and Supabase-backed state. Build customer-facing dashboard surfaces — deals, gamification, coaching, trust graph — with TypeScript, Tailwind, shadcn/ui, and D3. Operate WebSocket transcription sessions and async job pipelines reliably under real meeting load. Instrument with Sentry, harden auth (NextAuth :left_right_arrow: JWT :left_right_arrow: FastAPI service-to-service), and keep deploy pipelines green. Build with simulators: when you can't test against live meetings, generate realistic synthetic transcripts through our simulator service. Stack you'll work in Backend: Python 3.11, FastAPI, Uvicorn, PyJWT, Anthropic SDK, Deepgram, Neo4j, Supabase (Postgres + realtime), WebSockets Frontend: TypeScript, Next.js 15 (App Router), React 19, NextAuth 5, Tailwind, shadcn/ui, D3, Stripe Infra: Fly.io, GitHub Actions, Sentry, Supabase AI: Claude (Opus/Sonnet) for transcript analysis, action card generation, and agentic dev workflows What we're looking for 4+ years building production web applications, ideally across Python and TypeScript. Comfort designing and shipping features against an LLM API — prompt iteration, structured outputs, evals, cost/latency tradeoffs. Real Claude or OpenAI production experience required. Experience with multi-tenant SaaS patterns, JWT auth, and one or more of: graph databases, realtime systems, audio/transcription pipelines. Comfort with AI-assisted development workflows (Claude Code, Cursor, etc.) — not just as a code-completion tool, but as a way to plan and ship features. Bias toward shipping. Small surface area, high ownership, no committee.

Upwork's Governance, Risk & Compliance (GRC) team is seeking an experienced freelancer with a strong background in AI tool automation to help streamline and enhance our compliance workflows. You will work closely with our GRC team to identify automation opportunities, design and implement AI-driven solutions, and integrate tools that improve efficiency across risk assessments, policy management, audit preparation, and compliance monitoring. Key Responsibilities: Assess existing GRC workflows and identify high-impact automation opportunities Design and implement AI-driven automations using Claude AI to support intelligent document analysis, risk summarization, policy drafting, and compliance Q&A workflows Integrate AI tools with Vanta to enhance compliance monitoring, evidence collection, and control mapping Build automated workflows for risk tracking, audit preparation, and policy lifecycle management Document solutions and provide handoff training to internal GRC team members Required Qualifications: Deep knowledge of GRC principles, practices, and frameworks — including SOC 2, ISO 27001, ISO 27018, ISO 42001, PCI-DSS, and Microsoft SSPA — with the ability to translate compliance requirements into functional automation logic Demonstrated experience building AI and automation workflows, including LLM integration, prompt engineering, and API-based tool development Strong understanding of risk management methodologies, control frameworks, and audit readiness processes Experience operationalizing compliance programs, not just familiarity — you should be comfortable owning GRC workflows end-to-end Proficiency with no-code/low-code automation platforms and/or Python scripting Excellent written and verbal communication skills, with the ability to document technical solutions clearly for compliance audiences Preferred Qualifications: Prior hands-on experience working within a GRC or Information Security team Relevant certifications such as CISA, CRISC, CISSP, or ISO Lead Implementer/Auditor Experience with AI governance frameworks and emerging standards around responsible AI (aligned with ISO 42001) Familiarity with Upwork's platform or similar marketplace environments

I am building Dewy, an early-stage construction technology platform focused on construction buyout and subcontractor quote intelligence. The first MVP is intentionally narrow: users should be able to upload subcontractor quote/proposal documents and receive structured outputs showing included scope, exclusions, assumptions, qualifications, cost structure, alternates, allowances, and potential risk flags. I have already developed the product concept, construction logic, early workflows, and prototype direction using Codex/AI tools. I am not looking for someone to invent the product from scratch. I need a senior AI product engineer who can review what I have, determine what is usable, define a clean MVP architecture, and help turn the current direction into a working private beta. Initial scope: * Review the current prototype/code/product materials. * Identify what should be reused vs. rebuilt. * Recommend the MVP architecture and tech stack. * Define the AI document-processing workflow. * Design the structure for file upload, extraction, editable results, and export. * Help create a realistic build roadmap, timeline, and budget. * Potentially continue into hands-on MVP development if there is a strong fit. Ideal experience: * Full-stack SaaS / MVP development * AI / LLM application development * OpenAI API or similar model integrations * Document extraction or document intelligence workflows * PDF/DOCX parsing and structured data extraction * React / Next.js * Python * APIs and backend workflows * Supabase/Postgres or similar database experience * Vercel or similar deployment experience * Ability to work with a non-technical founder and translate business goals into a practical build plan This is not a full enterprise platform build yet. The first MVP should stay focused on one core workflow: Subcontractor quote documents in → structured buyout intelligence out. Please respond with: 1. Relevant AI/document extraction projects you have built. 2. How you would approach the MVP architecture. 3. Whether you recommend starting with an audit/roadmap before build. 4. Your hourly rate and availability. 5. Whether you are interested in ongoing build involvement after the initial review.