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.

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™

I’m looking for an AI Engineer to help build an automated red-teaming product based on open-source models. This is a short-term, hands-on project for around 2 months, with an expected commitment of about 20 hours per week. The goal is to build a specialized red-teaming engine that can generate adversarial prompts across different risk domains, severity levels, and attack strategies — then automatically run those prompts against target AI models to identify bad cases, failure patterns, and safety gaps. 🔍 What you’ll work on Build red-teaming systems on top of open-source LLMs, including fine-tuning, prompt optimization, evaluation pipelines, and model orchestration. Design automated prompt generation workflows across risk domains such as self-harm, hate, violence, sexual safety, misinformation, fraud, cyber, and other high-risk areas. Generate prompts across different harm levels, from benign edge cases to policy-borderline and clearly unsafe scenarios, while maintaining structured taxonomies and evaluation criteria. Run automated tests against target models such as Gemma, Llama, Qwen, or other open-source / closed-source models to surface jailbreak patterns, over-refusal, under-refusal, and policy inconsistencies. Build feedback loops that turn model failures into stronger red-team prompts, improved eval sets, remediation recommendations, and continuous safety testing. 🧠 What I’m looking for Hands-on experience with open-source LLMs, fine-tuning, LoRA / QLoRA, RAG, model evaluation, and LLM inference pipelines. Familiarity with AI safety, red teaming, adversarial prompting, jailbreaks, safety evals, or trust & safety systems. Ability to build end-to-end systems, including data pipelines, model serving, eval harnesses, scoring, dashboards, and automation workflows. Bonus if you’ve worked on model safety, content moderation, policy evaluation, agentic testing, or automated eval infrastructure. ⏳ Project setup Duration: around 2 months Time commitment: about 20 hours per week Format: flexible / remote-friendly Stage: early-stage build, from 0 to 1 🚀 Why this is interesting This is not about manually writing red-team prompts one by one. The goal is to build a scalable system that can continuously generate, test, categorize, and learn from model failures — helping teams understand where AI models break, why they break, and how to improve them. If you enjoy working with open-source models, AI safety, red teaming, and fast 0-to-1 product building, I’d love to chat. Feel free to DM me if this sounds like you, or if you know someone who might be a good fit.

Overview We're building an open-source CLI gateway for multi-agent AI orchestration — model-agnostic, MCP-native, and designed to bring any agent framework online with a single command. The repo is active, well-documented, and growing. We need an engineer to accelerate integration coverage and help attract open-source contributors. The Work Build agent templates and runnable examples for LangGraph, CrewAI, and similar frameworks Add LLM provider support (Groq, Mistral, Gemini, etc.) to the Hermes runtime Write clean, contributor-friendly code that models good PR hygiene Submit work via fork → PR → merge workflow on GitHub You Are Strong Python developer with CLI tooling experience Familiar with at least one of: LangGraph, CrewAI, LiteLLM, LangChain Comfortable with open source GitHub workflows (fork, PR, issues, reviews) Self-directed — you read docs, ask good questions, and don't wait to be unblocked Nice to Have Experience with MCP (Model Context Protocol) Familiarity with SSE, OAuth 2.1, or agent credential management Prior open source contributions Engagement Part-time to start, 20 hrs/week Fixed milestones per integration delivered Potential to grow with the project To Apply Share your GitHub profile and one example of open source work or a project that shows your Python and agent framework experience. https://github.com/ax-platform/ax-gateway

Want someone to go over our Retell prompts / get best practices, must be a retell partnered agency. Please share previous projects and the name of your business on retells site.

Description: I’m a non-technical founder building SoCalForeclosures.net — a subscription platform for Southern California real estate investors. Lead Members pay $149.99/month (capped at 8,000) to access scored off-market foreclosure and pre-foreclosure leads. Members work leads in a built-in CRM and sell deals through an internal marketplace to cash buyers (free tier or $99.99/month First Look). A working prototype is already built and deployed. Core features including user authentication, PayPal payments, marketplace (SoCalDeals), MyCRM pipeline, Phantom lead scoring engine, admin tools, and bot management screens are functional with demo data on a professional dark theme. Current honest status: The automated “bot army” that will pull real public distress signals from county recorders, courts, tax collectors, legal newspapers, trustees, and paid data sources is scaffolded but not yet actively ingesting live leads. A detailed signal catalog and county data sources are already documented. Project Scope (Fixed Price): I need an experienced senior full-stack developer to take the existing prototype and deliver a fully functioning, production-ready platform that is live and online. This is a single fixed-price engagement. Key deliverables for this project: Full code review and audit of the current Next.js + Express + PostgreSQL + Prisma codebase Polish and improve visual design and user experience across the member dashboard, lead feed, CRM, and marketplace so it looks professional for serious real estate investors Activate and productionize the real data pipeline (bot army) so it can ingest actual leads from public and paid sources according to the existing signal catalog Fix bugs, performance issues, technical debt, and operational problems Migrate hosting from the current European server to a reliable US-based provider with proper deployment pipeline, monitoring, and backups Stabilize the site so it runs reliably in production Deliver clear documentation and handover Important notes: This is not a from-scratch build. A substantial amount of the website and backend already exists and is deployed. The goal of this fixed-price project is to complete the remaining work and launch a fully functioning, live platform with real data capabilities. Once the website is successfully deployed live and online, we will negotiate a separate ongoing contract for maintenance, upgrades, new features, and additional work (including potential iOS and Android mobile apps). Tech stack: Next.js 14 (App Router) + TypeScript + Tailwind Express backend + PostgreSQL + Prisma Existing TypeScript bot framework (Playwright / Cheerio) PayPal payments, Twilio, Resend, Mapbox Ideal candidate: Senior full-stack developer with strong experience in TypeScript, Next.js, Prisma/PostgreSQL, and data pipelines or web scraping (public/government data sources) Experience taking prototypes to production, including hosting migrations and operational stability Comfortable working with an existing codebase and delivering clear recommendations to a non-technical founder Proactive, reliable, and quality-focused Project type: Fixed Price (one set price for the full scope above to get the platform fully live and functioning). To apply, please: Propose your fixed price for completing this project (review + polish + activate data pipeline + hosting migration + full production launch). Share 1–2 examples of similar past work (taking existing code to production, data platforms, or marketplaces). Briefly describe how you would approach reviewing and completing an existing prototype. Confirm your experience with US hosting providers and data pipeline / scraping work. Any questions about the current state or scope. Serious applicants will receive the full technical documents (Developer Brief, Signal Catalog, and County Data Sources) under NDA. Project owner is based in Southern California. US time zones preferred for easier communication.

We're seeking an experienced Contract AI Advisor to partner closely with our Head of Technology in developing and executing a comprehensive AI rollout plan for the organization. The ideal candidate will bring hands-on experience setting up AI infrastructure and designing training programs that enable teams to adopt AI tools safely and effectively. This role requires a strategic thinker who can assess our current technology landscape, recommend appropriate AI platforms and integrations, and create practical frameworks for responsible AI use across the organization. The advisor will work collaboratively to translate high-level AI strategy into an actionable implementation roadmap, ensuring both technical soundness and organizational readiness. This is a contract engagement ideal for a consultant or advisor with a track record of successful AI transformation initiatives in mid- large sized organizations. Experience with Claude a plus, as it is our current top choice.

We are looking for a skilled Full-Stack Developer with experience in AI and software engineering to support client communication and technical requirement gathering. In this role, you will be responsible for engaging directly with stakeholders through calls, understanding their technical and business needs, and accurately translating those requirements into clear, actionable documentation for the development team. The ideal candidate should have a strong technical background in full-stack development, AI systems, and modern software architecture, enabling them to ask the right questions, validate assumptions, and ensure that project requirements are correctly captured without ambiguity. You will act as a bridge between clients and engineering teams, ensuring smooth communication and reducing misunderstandings during project execution. Responsibilities include conducting requirement-gathering calls, clarifying technical specifications, documenting system needs, and collaborating closely with developers and product teams. Strong understanding of AI-driven systems, web technologies, and software development workflows is essential to perform effectively in this role. We are seeking someone who can combine technical expertise with clear communication skills, ensuring that complex requirements are translated into structured, developer-ready specifications that support successful project delivery.

Deliverable Requested from Developer Build a Google Workspace solution that: 1. Monitors incoming emails in slab-sewer@, rough@, and trim@ 2. Uses AI (Gemini, OpenAI, or similar) to classify severity and category 3. Automatically applies Gmail labels 4. Marks RED items as important 5. Runs automatically every few minutes 6. Requires no action from Project Coordinators Success Metric: A Project Coordinator can open their inbox and instantly identify which emails require immediate action versus which can wait until later in the day, without manually reading and sorting every message.

# 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.