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.

Looking for an expert developer to build a private, highly secure creative and financial workspace using the Google AI Studio / Gemini 1.5 Pro API. The absolute priority of this project is a 100% complete data migration. You will take a 6-week raw chat log containing precise financial checking/savings ledgers, business blueprints, and long-form biographical story notes, and ingest it flawlessly into a permanent vector database. You will connect this repository to the Gemini API so the system maintains permanent context retention across both a laptop and an Android phone without losing a single digit or syllable. Must deliver a clean, private, password-protected web interface that allows for seamless text dialogue, image uploads (for inventory tracking), and audio processing. Security, privacy, and flawless data retention are non-negotiable.

The Role: As a Software Engineer on our AI Infrastructure team, you will help design the core systems that power Prism AI’s generative AI platform. You will help build infrastructure and tools that ensure the reliability, performance, quality, and availability of our AI system. Our mission is to make Prism AI the most reliable and user friendly generative AI platform in the world. You will partner closely with our cloud infrastructure team, product team, and performance team to deliver infrastructure that bridges the gap between our customer and the ultra-performant proprietary Prism inference engine. Key Responsibilities: Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines Build and maintain core backend services such as LLM CI/CD pipeline, control plane, and model serving systems Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers Building frameworks and safeguards to ensure Prism AI has the best model quality in the industry Collaborate with performance, training, and product teams to translate research and product needs into infrastructure solutions Participate in code reviews, technical discussions, and continuous integration and deployment processes Minimum Qualifications: Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience) 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems Strong programming skills in Python, Go, or a similar language Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes, etc.). Basic understanding of LLM knowledge (e.g., context length, disaggregated prefill, KV cache memory estimation, etc) Preferred Qualifications: 5+ years of experience in software engineering, with a focus on infrastructure or machine learning systems Experience with open source inference engine like vLLM, Sglang, or TRT-LLM Contributions to open-source infrastructure or ML projects Experience in building large scale ML/MLOps infrastructure

I need a developer to build an AI visibility audit tool for destination marketing. The core logic is already defined and I have a full spec. I need someone who can build it clean and ship it. What the tool does: it queries ChatGPT, Gemini, Claude and Perplexity with a fixed set of real traveler questions, captures whether a destination shows up and where its competitors land, scores the result, and drafts a short report. Roughly 15+ questions, each run a few times per platform, with web search enabled. What I need built: The query engine across all three platforms, running on my own API keys Integration with my existing scorecard backend A gated flow: a personal emailed link that runs once per user, results delivered by email A saved-run database I can log into and review, so every run is stored from day one Built to be re-run on a schedule later (this becomes an ongoing monitoring product) Two non-negotiables: It runs entirely on my API accounts and keys. Billing and ownership sit with me. I own all code and IP outright. This is a defined, finish-and-ship project, not open-ended. I'll share the full spec with candidates who look like a fit. US-based candidates only. Skills LLM / OpenAI API, Gemini API, Perplexity API, API integration, Python (or your stack — tell me), backend development, database design, prompt engineering If interested, please respond with the following answers to be taken seriously: Describe a tool you've built that calls LLM APIs in production. What did it do and what was your specific role? How would you handle the fact that AI answers vary run to run? How do you make a score that holds up to scrutiny? What's your approach to keeping per-query API costs controlled at volume? Rough estimate on timeline and cost for a project scoped like this.

We are seeking an AI Stack Developer to build and integrate AI solutions for automating email newsletters, monthly reporting, project dashboards, and social media content planning. The ideal candidate will have experience in developing AI stacks and integrating them with existing systems to enhance efficiency and accuracy. Responsibilities include designing AI workflows, implementing data analysis tools, and ensuring seamless integration with our current infrastructure.

I need a Discord bot built that posts positive expected value (+EV) sports betting picks to a channel, plus esports match schedules/scores. I have the full technical spec already written — APIs picked, EV formula defined, architecture outlined. This is integration work, not research. What's provided: Working API keys for The Odds API (sports odds) and PandaScore (esports stats) — I'll provide on hire Complete EV calculation formula (Python, ready to use) Full architecture spec (polling schedule, caching approach, bookmaker lists) Core deliverables (must-have, fixed price): Discord bot that connects to my server Scheduled polling (1-2x daily, not live) of The Odds API for sports moneylines + player props EV calculation comparing soft-book odds (DraftKings/FanDuel/Caesars) against Pinnacle as the sharp reference Caching layer (database) so the bot never calls the API live per Discord command Bot posts flagged +EV picks to a designated channel, above a configurable EV threshold PandaScore integration for esports match schedules/results posted to a separate channel Basic error handling with retry/backoff (no runaway API costs) Documentation: how to run it, how to add new sports/leagues, how to change the EV threshold Stretch goal (only if time allows within budget — not required): OddsPapi integration for esports moneyline EV detection (this API is unverified/free-tier, so treat as experimental) Tech preferences: Python or Node.js, whichever you're stronger in. Open to your hosting recommendation (needs to run 24/7 cheaply). Budget & Payment Structure: $500 fixed price, split into milestones Milestone 1 ($150) — Foundation: Discord bot connects to server, The Odds API integration pulling live sports odds data, caching layer working. Paid on demo of working data pull + bot online in server. Milestone 2 ($200) — Core Logic: EV calculation implemented and verified accurate against manual spot-checks, picks posting to Discord channel automatically on schedule. Paid on demo of at least 3 correctly-calculated +EV picks posted live. Milestone 3 ($150) — Esports + Polish: PandaScore esports schedule/results integration, error handling/retry logic, documentation delivered. Paid on final delivery + handoff call. To apply: Tell me your estimated hours for each milestone, and confirm you've worked with Discord bots + REST API integrations before.

We're looking for a developer to build a lean, working Proof-of-Concept of an automated pipeline that ingests podcast episode audio, generates a clean transcript with speaker diarization and timestamps, and uses an open-source NotebookLM alternative (Notex or Open Notebook) to automatically produce a suite of repurposed content assets — show notes, episode summaries, social media posts, blog drafts, and pull quotes. The goal is to validate the end-to-end workflow on 2–3 sample episodes, not to build a full production platform yet. We want to see the plumbing work cleanly before investing in scale. Envisioned stack: n8n for orchestration, a speech-to-text API (Deepgram, AssemblyAI, or Whisper), a lightweight DB (Supabase or PostgreSQL), and an open-source NotebookLM alternative as the content generation engine. The whole system should be self-hostable via Docker. We're open to the developer's recommendations on the best tools and tradeoffs. Deliverables include a working n8n workflow, Docker-compose setup, a short README, demonstration on 2–3 sample episodes we provide, and a brief written recommendation on Notex vs. Open Notebook for scaling this pipeline to ~500 episodes/year. Required skills: n8n (or similar orchestration), speech-to-text APIs, Docker / self-hosted deployments, hands-on experience with NotebookLM alternatives or RAG-based content engines, LLM prompt engineering for structured output, and PostgreSQL / Supabase basics. Nice to have: Prior podcast or media-tech automation work, pgvector / RAG experience, structured output via JSON schema or function calling, and experience scaling automation pipelines. To apply, please include: a short overview of your automation / AI pipeline background, specific experience with n8n + STT APIs + open-source NotebookLM alternatives, links to GitHub or prior workflows, a 2–3 sentence note on whether you'd recommend Notex or Open Notebook for this use case and why, and your estimated turnaround time. This is a fixed-budget POC (~$100). If the workflow is clean, reliable, and well-documented, we plan to expand it into a full production build (client portal, human-in-the-loop editor, admin dashboard, scaling to 500+ episodes/year) with a significantly larger budget.

Sphere Inc. is building a new AI-powered SaaS platform for the U.S. healthcare industry. We're looking for a senior engineer who enjoys building products from scratch, making technical decisions, and shipping production-quality software. This is an MVP that will quickly transition into production, so we're looking for someone who is comfortable owning architecture, development, deployment, and AI integration. We're developing a HIPAA-compliant Care Coordination Platform that helps physicians, nurses, and care coordinators manage chronic care patients more efficiently. A patient with diabetes and hypertension visits a primary care clinic. Instead of manually reviewing hundreds of pages of clinical notes, lab results, discharge summaries, and specialist referrals, the provider uploads the patient's records. The AI platform will: - Extract structured medical information from uploaded documents - Generate concise clinical summaries - Highlight medication conflicts and missing follow-ups - Detect abnormal lab trends - Recommend preventive care actions based on clinical guidelines - Generate visit notes and patient-friendly summaries - Allow physicians to approve, edit, or reject AI-generated recommendations - Maintain complete audit trails for HIPAA compliance The system must never expose PHI to unauthorized users and must meet healthcare security best practices. You'll work directly with our founders to design and build the MVP. Responsibilities include: - Design scalable backend architecture - Develop responsive React/Next.js frontend - Build secure REST APIs - Integrate OpenAI, Anthropic, or Azure OpenAI - Implement Retrieval-Augmented Generation (RAG) - Build document ingestion pipelines - Implement vector search - Build role-based access control - Design PostgreSQL database schema - Implement authentication and authorization - Deploy production infrastructure on AWS or Azure - Write automated tests - Optimize AI performance and costs Candidates should understand: - HIPAA Security Rule - PHI handling - Encryption at rest and in transit - Audit logging - Role-based permissions - Secure cloud architecture - Least-privilege access - Secrets management - BAA-aware cloud services Previous healthcare or medical SaaS experience is highly preferred. To Apply Please include the following in your proposal: - Links to recent AI SaaS or healthcare projects - Your GitHub profile - A brief description of your HIPAA or healthcare experience - 5–10 minute Loom video walkthrough of a HIPAA-compliant AI or SaaS project you personally built, highlighting the architecture, technical decisions, and your specific contributions.

# 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.

Senior Full-Stack Engineer — Canvas Medical EHR Lead Contract · Monthly retainer · Remote (US-based) · Ongoing -= DONT APPLY IF =- Not physically present in USA you're an AGENCY. I need 1 person. no middleman Generalists and "I can pick up Rails" applicants will not be a fit. If we can't see your projects on github dating back years (temp) you're not serious. Anyone who's thinking of making someone else do the work, go away. No time for games. Summary I'm hiring a senior full-stack engineer to lead the build-out and launch of a longevity-medicine platform: B2B formulary ordering for licensed providers, plus a patient prescription and reorder portal, built on Canvas Medical (a programmable, ONC-certified EHR). The design and product logic are set. I need someone who configures the EHR, builds the integrations, finishes the frontend, ships it, and keeps it running. This is not a learn-on-the-job seat. It is a finisher seat. What you'll own Configure and extend Canvas Medical to the prescription, formulary, and patient-portal workflow by writing Python plugins on the Canvas SDK Integrate the platform over the Canvas FHIR (R4) API, including provider registration, ordering, billing, and the patient portal Finish and ship the product frontend and bind it to the API Handle deployment, uptime, and ongoing monthly support and iteration after launch Stack Python (Canvas SDK plugins run inside the EHR) Canvas FHIR R4 API (FastAPI-based); Django ORM for data access Frontend: finishing an existing web frontend and wiring it to the API Healthcare data handling under HIPAA Must have Senior-level Python. Non-negotiable. The SDK is Python. Hands-on FHIR / healthcare data integration. This is the differentiator and the first thing I screen for. Experience with Canvas Medical or a comparable EHR or health-data platform (Medplum, Redox, Health Gorilla, or similar) HIPAA and PHI handling experience. This is a live EHR with patient data, not a storefront. Frontend competence to finish a web app and connect it to a REST/FHIR API A track record of shipping. Links to live work, not a list of frameworks. Strong async communication and reliable follow-through Nice to have Direct Canvas SDK plugin experience (Events, Data, Effects) Prior digital-health, telehealth, or pharmacy-tech product work Experience taking a stalled or unfinished product across the finish line Engagement Contract, paid on a monthly retainer Remote, US-based for this lead role Long-term: build phase first, then ongoing support. I'm looking for a durable relationship, not a one-off. To apply In your reply, include: Links to things you've shipped, FHIR and EHR work prioritized One example of a project you took from stuck to done, and what you did? Whats your favorite car? Your monthly availability in hours? Thanks