We’re looking for an experienced artificial intelligence engineer to join the revolution, using deep learning, neuro-linguistic programming (NLP), computer vision, chatbots, and robotics to help us improve various business outcomes and drive innovation. The engineer will join a multidisciplinary team helping to shape our AI strategy and showcasing the potential for AI through early-stage solutions. This is an excellent opportunity to take advantage of emerging trends and technologies and make a real-world difference.

AI Architect & Autonomous Agent Engineer (Full-Time, US-Based) Own a Live Production Agent Fleet WHAT THIS IS I run a small, profitable company with an unusual amount of automation behind it. A fleet of autonomous AI agents runs our internal data operation unattended for roughly 12 hours a day, every day. It is real production infrastructure that the business depends on. This is not a "build me a chatbot" job and it is not greenfield. The system exists, it runs daily, and mistakes cost real money. Multiple independent pipelines run in parallel, each doing multi-stage automated research, each calling paid third-party APIs at several points, each with its own quality gates and delivery step. Tens of thousands of records have moved through it. I have been operating and extending this system myself. I need someone to own it so I can stop being the bottleneck. This is an architect role and a builder role at the same time. You will design the system AND write the code AND debug it at 6pm when an agent has done something confident and wrong. There is no team under you to hand it off to. If that split appeals to you, keep reading. I will describe the domain and the specifics on a call, under NDA. What I can tell you publicly is the engineering problem, which is below and is genuinely the interesting part. WHAT YOU WOULD OWN 1. ARCHITECTURE AND AGENT DESIGN - Own the overall design: how the pipelines fit together, where state lives, what runs where, and what happens when any piece fails - Build and maintain autonomous agents that run for hours without a human watching, using Claude Code and Codex - Design the guardrails: quality gates, fail-closed checks, regression tests,and audit trails so an agent cannot silently ship bad work - Debug agents that did the wrong thing confidently, which is the hard part 2. MULTI-DEVICE FLEET ORCHESTRATION - Scale from one machine to many machines running the same pipelines at once - Solve the coordination problems that come with that: shared claim and lock systems so two machines never do the same paid work twice, distributed state, race conditions, safe failure modes - Build the setup and sync tooling so a new machine can be onboarded quickly and every machine runs identical, current logic 3. INTEGRATIONS AND DATA PLUMBING - Cloud spreadsheets and file storage used as coordination and reporting layers across machines - Several third-party vendor APIs, some of them metered and billed per call - Reporting that a non-engineer can actually read and trust 4. QUALITY AND COST CONTROL - Every paid API call should be justified and never duplicated - Build measurement into the system so we know our unit cost and can improve it deliberately, not by guessing WHO THIS IS FOR You will do well here if: - You have shipped agentic systems that run unattended, not just prompts that work in a demo - You think like a systems engineer: idempotency, locking, retries, race conditions, failing closed, and knowing the difference between "it returned 200" and "it actually worked" - You are comfortable in Python, APIs, and the command line - You test your own work adversarially and assume your first answer is wrong - You can explain a technical tradeoff to me in plain language without making me feel stupid or hiding the risk - You are comfortable working on something you cannot put in a public portfolio You will not do well here if you need tickets written for you, if you have only worked on greenfield projects, if you want to architect without implementing, or if you are more excited about model choice than about whether the pipeline is correct at 2am with nobody watching. LOGISTICS - Full-time, long-term. This is an ownership role, not a one-off project. - US-based required. Significant overlap with US Eastern hours. - NDA before we get into specifics. HOW TO APPLY Skip the generic cover letter. I will read all of these and ignore anything that looks templated. Please answer these three questions: 1. Describe an autonomous system you built that ran without supervision. What broke, how did you find out, and what did you change so it could not happen again? 2. Two machines are running the same pipeline against a shared queue of work items. Each item costs money to process. How do you make sure no item is ever paid for twice, and what happens when one machine dies mid-task? 3. What is a mistake an AI agent made in something you built that you did not catch until it had already caused damage? Short and specific beats long and polished. If your answer to #2 is one paragraph and correct, you are ahead of most applicants.

I need someone who has experience with AI Apps that are deals with construction estimating and a fast learner. Must be able to learn the App and teach it to another worker.

I'm early in my career and self-taught, working on breaking into AI engineering, and I want one honest hour with someone actually in the trenches — not a sales pitch, not encouragement. Just a candid conversation, video on. What I'm after: your unvarnished read on how someone in my position actually breaks in, where the field is really headed, what companies are truly paying to have built or solved, and — if you were starting out today with what you know now — exactly what you'd do over the next 6 months. About me: no CS degree, no traditional pedigree — I've been learning by shipping real projects (I've built a couple of small live AI products end-to-end: RAG, an agent, automation). I know enough to ask good questions and I'll come prepared so we use the time well. I'd also love to show you one thing I built for 60 seconds of brutally honest feedback.

Help create my AI clone and have it do YouTube videos.

Building a machine learning platform for an insurance related product, with a focus on data pipelines, feature and label design, model development, deployment planning, monitoring, and business impact. Looking for an experienced MLOps or applied ML engineer to provide weekly mentorship through structured project check ins. The implementation will remain entirely my responsibility. Each meeting will focus on the current state of the project. I will provide detailed context on what has been completed, the decisions being considered, current blockers, and the next stage of work. The mentor will review that specific situation, challenge assumptions, identify gaps, and provide direct feedback based on how the issue would be handled in a real production environment. The goal is not general instruction. Feedback should be specific to the project, its architecture, data, constraints, and business use case. Meeting Structure: - Approximately 60 minutes per week - Review of progress since the previous meeting - Discussion of current technical and business decisions - Review of architecture, pipelines, model design, or deployment planning - Identification of risks, missing requirements, and unnecessary complexity - Clear recommendations and next steps for me to complete independently Scope of Guidance: - Business use case and ROI analysis - Data architecture, quality, lineage, and source contracts - Record grain, joins, and entity matching - Feature engineering and leakage prevention - Label and outcome design - Model evaluation and business metrics - Experiment and dataset versioning - Batch and real time deployment - Monitoring, drift, retraining, and rollback - Reliability, privacy, security, and governance Work Expectations: This is a meeting based mentorship role only. The mentor will not be expected to: - perform implementation work outside scheduled meetings - write or maintain the codebase - prepare separate reports or deliverables between meetings - manage the project - provide ongoing asynchronous support - take ownership of delivery Required Experience: - Professional experience building or operating production ML systems - Strong understanding of MLOps, data engineering, deployment, and monitoring - Experience reviewing real technical systems and making practical recommendations - Ability to explain tradeoffs clearly and give direct, specific feedback - Willingness to challenge weak decisions rather than provide generic advice Working Style: Direct communication and practical feedback are important. I will prepare the project context and questions before each meeting, complete the work independently afterward, and return with results for review. Initial Engagement: The engagement will begin with one paid consultation. The session will be used to review the project, discuss the expected mentorship style, and determine whether recurring weekly meetings are a good fit.

Building a machine learning platform for an insurance related product, with a focus on data pipelines, feature and label design, model development, deployment planning, monitoring, and business impact. Looking for an experienced MLOps or applied ML engineer to provide weekly mentorship through structured project check ins. The implementation will remain entirely my responsibility. Each meeting will focus on the current state of the project. I will provide detailed context on what has been completed, the decisions being considered, current blockers, and the next stage of work. The mentor will review that specific situation, challenge assumptions, identify gaps, and provide direct feedback based on how the issue would be handled in a real production environment. The goal is not general instruction. Feedback should be specific to the project, its architecture, data, constraints, and business use case. Meeting Structure: - Approximately 60 minutes per week - Review of progress since the previous meeting - Discussion of current technical and business decisions - Review of architecture, pipelines, model design, or deployment planning - Identification of risks, missing requirements, and unnecessary complexity - Clear recommendations and next steps for me to complete independently Scope of Guidance: - Business use case and ROI analysis - Data architecture, quality, lineage, and source contracts - Record grain, joins, and entity matching - Feature engineering and leakage prevention - Label and outcome design - Model evaluation and business metrics - Experiment and dataset versioning - Batch and real time deployment - Monitoring, drift, retraining, and rollback - Reliability, privacy, security, and governance Work Expectations: This is a meeting based mentorship role only. The mentor will not be expected to: - perform implementation work outside scheduled meetings - write or maintain the codebase - prepare separate reports or deliverables between meetings - manage the project - provide ongoing asynchronous support - take ownership of delivery Required Experience: - Professional experience building or operating production ML systems - Strong understanding of MLOps, data engineering, deployment, and monitoring - Experience reviewing real technical systems and making practical recommendations - Ability to explain tradeoffs clearly and give direct, specific feedback - Willingness to challenge weak decisions rather than provide generic advice Working Style: Direct communication and practical feedback are important. I will prepare the project context and questions before each meeting, complete the work independently afterward, and return with results for review. Initial Engagement: The engagement will begin with one paid consultation. The session will be used to review the project, discuss the expected mentorship style, and determine whether recurring weekly meetings are a good fit.

We are looking for a skilled, hands-on AI Engineer to help us build and optimize our AI product. You will be responsible for designing the AI architecture, integrating modern LLMs/frameworks, and ensuring our AI pipeline runs efficiently, reliably, and accurately in production. Responsibilities Design, build, and deploy custom AI solutions (LLM integration, RAG, AI agents, or fine-tuning). Build robust prompt engineering pipelines, function-calling workflows, or structured output mechanisms. Implement vector databases (e.g., Pinecone, Weaviate, Qdrant, ChromaDB) for semantic search and retrieval. Optimize latency, API costs, and context window efficiency across LLM providers (OpenAI, Anthropic, open-source models). Connect AI models to backend services via REST APIs / webhooks. Implement evaluation metrics (hallucination detection, retrieval accuracy, output validation). Required Skills & Qualifications Languages: Python (strong expertise required), TypeScript/Node.js (a plus). AI / ML Tooling: LangChain, LlamaIndex, AutoGen, CrewAI, or direct SDK integrations (OpenAI, Anthropic, Hugging Face). Databases: Vector databases (Pinecone, Chroma, Qdrant, pgvector) + relational/NoSQL DBs. Deployment & Cloud: Docker, AWS / GCP / Azure, FastAPI / Flask, Serverless architectures. Core Concepts: In-depth understanding of Embeddings, RAG, Fine-Tuning, Function Calling, and Agentic Workflows. Preferred (Nice to Have) Experience deploying open-source models locally or on dedicated hardware (vLLM, Ollama, Hugging Face TGI). Experience with fine-tuning techniques (LoRA, QLoRA). Background in frontend AI UI integration (Vercel AI SDK, Streamlit, Gradio).

We are looking for an experienced AI Agent Product Engineer to help us build production-ready AI agents that can be used directly by businesses and the general public. We are not looking for someone who only builds prototypes, automations or basic chatbot demos. We need someone who has experience turning AI agents into reliable, scalable products with real users. You will work on AI products for Servicia, including Agents on Demand and Companies on Demand, as well as AI agents that may be offered to external clients through our AI initiatives. The ideal candidate has experience with: OpenAI API and agentic AI frameworks OpenAI Agents SDK, LangGraph or similar technologies Tool calling and multi-agent workflows RAG and knowledge bases Memory and context management API integrations Authentication and user management Multi-tenant SaaS applications Human-in-the-loop approvals Guardrails and permissions Monitoring, logs and observability AI evaluations and reliability testing Managing token and inference costs Payments and subscriptions Deploying AI products to real users Full-stack development Experience with voice agents, WhatsApp, Gmail, Google Drive, CRM, ERP or enterprise software integrations is a strong plus. We are particularly interested in developers who have already launched AI agents or AI-powered SaaS products used by real customers. This can begin as a freelance project, with the possibility of becoming a long-term relationship. We expect to develop multiple agents and eventually create a reusable platform that allows us to rapidly build, customize, deploy and monetize AI agents.

We're building an AI Research Copilot for our financial analytics platform. The Copilot will help investors and financial professionals analyze market data, company financials, news, and earnings using natural language. This is a production AI project, not a basic chatbot. Responsibilities Build a production-ready AI Research Copilot Design and implement RAG pipelines Integrate LLMs with our financial data and APIs Implement semantic search and tool calling Build conversation memory and streaming responses Add source citations and improve response accuracy Optimize performance and reduce hallucinations Required Skills Python OpenAI, Claude, or Gemini APIs RAG and vector databases (Pinecone, Qdrant, pgvector, Weaviate, etc.) LangGraph, LangChain, LlamaIndex, or similar frameworks API integration Prompt engineering Production AI application experience Please include: AI products or copilots you've built Your experience with production RAG systems Your preferred AI architecture for this project Briefly explain how you reduce hallucinations and provide trustworthy AI responses. We're looking for an experienced engineer who can build scalable, production-quality AI systems and collaborate long term.