We’re hiring a Senior Full-Stack AI Engineer to build an MVP for an LLM-powered recommendation platform. The role involves developing questionnaires and structured AI output to enhance user experience. The ideal candidate will have experience in AI and full-stack development, with a focus on delivering efficient and effective solutions.

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

Title: Backend Developer — AI Data Pipeline, Vector DB & Real-Time Push API Post: We are building an automated backend system that continuously crawls public web sources, processes and indexes content using AI, and delivers updates via webhooks. Looking for someone who has built this type of system before and can move fast. NDA required before project details are shared. What you’ll build: • Web crawler network —. • AI processing pipeline — cleans, deduplicates, chunks, and embeds ingested content into a vector database using an LLM embedding model. Quality scoring and incremental updates required. • Push API — monitors for significant content changes and delivers updates via webhook endpoints automatically. Includes configurable push schedules per subscriber, REST query endpoint, API key authentication, and token usage tracking per key. Tech stack (flexible — use what you know best): • Python (FastAPI) or Node.js • Any vector DB — Pinecone, ChromaDB, Supabase • Any LLM API — Anthropic or OpenAI • Any scheduler — n8n, APScheduler, cron • Redis for queue management • Railway, Render, or AWS for deployment Requirements: • NDA signed before kickoff — non-negotiable • Must have built RAG pipelines or vector DB systems in production — not tutorials • Must have experience with web crawlers and scheduled job pipelines • Must have experience with webhook delivery systems • GitHub or portfolio showing relevant deployed work required • 95%+ Job Success Score preferred • Individual contractors only — no agencies To apply include: • Example of a similar system you’ve built — web crawler, RAG pipeline, or push notification API • Your preferred stack for this type of build • Brief technical approach in 3–5 sentences • Hourly rate and availability to start Budget: $50–$80/hr Timeline: 3 weeks — focused sprint with daily check-ins

US-BASED CONTRACT FULL-STACK / AI ENGINEER FOR HEALTHCARE PRODUCT Remote, US-Based Contractor | East Coast Time Required | Home Health AI Product About Scribble Scribble is the top-rated AI platform for home health purpose-built to give clinicians their time back. Home health agencies run on documentation: visit notes, care plans, prior authorizations, compliance paperwork - we help automate these processes. Project Overview We are looking for one or more experienced US-based contractors to help us move faster across several important areas of our product. We need hands-on builders who can scope clearly defined product needs, ask the right questions, and independently deliver high-quality work. This is not a narrow ticket-taking role. We are looking for people who can own meaningful pieces of the product, communicate clearly, work asynchronously, and make steady progress without heavy day-to-day management. Depending on your strengths, the work may focus on mobile, backend, AI workflows, and EMR robotic process automation. You will ideally own end to end development including testing. What We Need Help With • Build and improve visit types and clinical documentation workflows for home health use cases • Improve accuracy of AI-generated outputs, including prompt design, evaluation, testing, and workflow refinements • Integrate completed visits and documentation into EMR systems using APIs where available and robotic process automation where needed • Work across React Native mobile app features, Node.js backend services, OpenAI/LLM workflows, and automated testing • Debug production issues, improve reliability, and help us ship quickly without sacrificing quality • Troubleshoot and fix bugs quickly, and make product improvements based on customer feedback • Translate product requirements into practical technical plans and independently execute against them • Document decisions, provide frequent updates, and proactively flag risks, blockers, and trade-offs • Potentially collaborate with team members across different parts of the roadmap Skills We Are Looking For Required • Advanced use of Claude Code • US-based contractor with East Coast time zone availability required • Strong experience with React Native mobile development • Backend experience with Node.js, APIs, databases, authentication, and production debugging • Hands-on experience with OpenAI or other LLM APIs, prompt engineering, structured outputs, and AI workflow testing • Strong testing mindset, including unit tests, integration tests, regression testing, and quality checks for AI outputs • Ability to work independently from a product goal, break it into technical tasks, and deliver without constant direction • Excellent written and verbal communication; concise updates, clear questions, and proactive status reporting are essential • Speed and quality are both must-haves: we need someone who can move quickly while still shipping reliable, well-tested work • Comfortable working with sensitive healthcare data and following HIPAA-aware, security-conscious development practices Strongly Preferred • Meaningful healthcare experience is strongly preferred, especially in home health, clinical documentation, EMR/EHR workflows, HIPAA-aware development, or regulated healthcare environments Contract Details • Contract role for a US-based independent contractor • US-based candidates only; East Coast time zone availability is required • Part-time or project-based to start, with potential for ongoing work • Minimum availability of 20 hours per week is required • We may hire multiple contractors based on specialty, fit, and availability • Clear deliverables, frequent communication, and fast iteration cycles • Hourly rate or fixed-price milestones can be discussed based on scope and experience • Selected contractors will need to sign appropriate IP assignment, NDA, and Business Associate Agreement documents before accessing sensitive product or healthcare data • We may request and check references before starting a larger engagement

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.

I’m running a real estate investment platform called ToInvested.com. The project is about 90% finished, and most of the code was built with Claude together with another engineer. Now I need a senior engineer to step in, review the full product carefully, test every major workflow, and help verify that everything is working correctly before it goes live. This is not just a “write more code” role. I need someone who can look at the platform like a real product, find hidden bugs, catch weak logic, test edge cases, review the AI-generated code, and tell me honestly what is ready and what still needs fixing. Because this is a real estate investment platform, accuracy and trust matter a lot. Users may rely on property data, investment logic, calculations, and AI-driven insights, so even small issues can create a serious problem later. The ideal person has strong full-stack experience, understands AI-assisted development, and has a good testing mindset. Real estate tech experience would be a big plus, especially with property platforms, investment tools, marketplaces, mortgage systems, or financial workflows. My main goal is simple: I want someone to break the project before real users do. If you’re the kind of engineer who can take a nearly finished product, test it deeply, clean up weak areas, and help make it production-ready, I’d be happy to talk.

About the Role: We are seeking a highly qualified Senior Machine Learning and Natural Language Processing Engineer with deep expertise in sentence parsing, contextual understanding, categorization, and language extraction to support and advance Sybal’s Proof of Governance® (PoG™) platform. This role blends advanced NLP engineering, full-stack development, and enterprise-grade deployment. You will design custom NLP models, build scalable AI-driven services, and deploy production-ready applications that transform raw policy and technical language into structured governance intelligence. You must be a senior-level full-stack engineer proficient in Python, Django, JavaScript, HTML, and CSS, with the ability to dockerize and deploy applications into production environments. Experience commercializing enterprise AI applications is required. You should also be familiar with using agentic AI tools in a development context—for debugging, workflow acceleration, rapid prototyping, and improving engineering efficiency. ________________________________________ Key Responsibilities: NLP & Machine Learning Engineering: • Build advanced NLP models for sentence parsing, context detection, semantic analysis, entity extraction, and policy language interpretation. • Develop hybrid ML + rule-based systems that support governance modeling and policy decomposition. • Create pipelines for text ingestion, annotation, categorization, and structured language extraction. • Design evaluation frameworks for accuracy, drift, reliability, and linguistic precision. • Research and implement non-LLM NLP methods relevant to governance and policy analysis. Full-Stack Engineering: • Develop production-ready applications using Python (spaCy, NLTK, TensorFlow, or PyTorch to build and optimize NLP models), Django, JavaScript, HTML, CSS, and modern tooling. • Further develop NLP models for PoG™ Feature enhancements. • Develop and maintain secure, scalable REST APIs and backend services. • Integrate ML components seamlessly into PoG™’s architecture. Production Deployment & DevOps: • Dockerize machine learning pipelines and full-stack applications for uniform deployment. • Deploy and manage services in cloud production environments (AWS, GCP, or Azure). • Set up CI/CD pipelines, monitoring, observability, and scalable containerized processes. • Ensure production performance, uptime, and system reliability. AI Automation for Engineering Efficiency: • Use agentic AI tools to assist with debugging, test generation, workload orchestration, and internal development workflows. • Integrate AI-assisted coding tools responsibly into engineering processes. Contribute to the Proof of Governance® Platform: • Build NLP and ML components that strengthen PoG™’s ability to: • Map policy language into structured governance data • Detect enforceability gaps • Identify policy dependencies and contextual interactions • Deliver measurable, enforceable governance intelligence • Collaborate with PoG™ architects to extend platform intelligence across governance domains. ________________________________________ Qualifications: Required Skills & Experience: • 6–10+ years of software engineering experience with specialization in ML and NLP. • Mastery of sentence parsing, syntax/semantic analysis, dependency modeling, and contextual extraction. • Proven experience commercializing enterprise AI or ML-driven applications. • Proficiency in: o Python o Django o JavaScript o HTML / CSS • Demonstrated ability to dockerize applications and deploy them into production. • Strong understanding of ML architecture, data modeling, distributed systems, and backend engineering. • Experience using agentic AI tools for engineering workflows (debugging, code analysis, test generation). • Strong cloud engineering experience (AWS, GCP, Azure). Preferred Qualifications: • Background in computational linguistics or structured policy analysis. • Experience with ontologies, taxonomies, or governance modeling. • Prior work in regulated, audit-heavy, or mission-critical environments. • Contributions to high-scale enterprise software platforms. ________________________________________ Who You Are: • You excel in both advanced NLP engineering and full-stack software development. • You can design systems end-to-end—from custom algorithms through front-end integration to production deployment. • You understand how to use AI to accelerate development processes. • You are driven by building systems that transform governance from assumption to measurable, enforceable proof. • You are excited to contribute to the continuous evolution of PoG™

We are building a production SaaS platform called MarketLens, designed for investment teams and analysts who need real-time market intelligence combined with AI-generated insights and portfolio tracking tools. What You’ll Do - Build and maintain the core backend services in Python (FastAPI / Django) - Develop frontend dashboards using React (TypeScript preferred) - Integrate real-time data pipelines (WebSockets, streaming APIs, or message queues) 1. Implement AI features such as: 2. Market summarization using LLMs 3. Portfolio risk explanations 4. Automated insight generation from time-series data - Design and maintain scalable APIs for analytics and user data - Work on subscription and billing logic (Stripe integration) - Improve system performance, especially around data freshness and dashboard latency - Participate in architecture decisions for scaling AI + data workloads Our clients are small to mid-sized hedge funds and financial advisory teams who currently rely on fragmented tools like Bloomberg exports, spreadsheets, and separate analytics dashboards. MarketLens aims to unify all of this into a single workflow.

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.

Add Tests, Security Audit for Credit Cards, Possible Refactor: Vibe-coded Social Graphify + Lovable: Do not use ai to write your proposal, or for any of your communication. I do not need AI or an AI detection tool to know. It's obscene. I can't trust Claude to make tests. It always slyly reverts when making a productive change to changing the tests to mock data. So I need a human to implement them. Full-stack web dev js, python, llm's. Additional background or interest in: Graph Theory, Graph Neural Nets, Graphical Probabilistic Models, Bayesian Neural Nets, Category Theory, Pre-Deep Learning Natural Language Processing (Cfg's, etc.), Semantic Web Tech (rdf/owl/xbrl), Library Sciences, Pre-LLM Machine Learning (+Stat/Econometrics/etc.), Federated Learning and Crypto is appreciated. Do not use ai to write your proposal, or for any of your communication. I do not need AI or an AI detection tool to know. It's obscene.