AI Engineer - Contract to Hire - W2

Contract to Hire Role- W2 Only

No C2C or 3rd Party

MUST SKILL: Production Experience (Production Solutions Personally Built or Owned)

Candidate Profile

The successful candidate is a strong software engineer who has genuinely shipped AI-powered features into production, not just prototyped them. You re comfortable operating with limited requirements, you ask good questions rather than waiting for detailed direction, and you can point to work you ve personally built, deployed, and supported. You care as much about testing, observability, and reliability as you do about the AI itself, and you can clearly explain the tradeoffs behind your architectural and technology choices.

Required Qualifications

• Bachelor s degree or equivalent experience in Computer Science, Engineering, or a related technical discipline.
• 3+ years of professional software engineering experience, including ownership of production systems.
• Strong programming skills in Python, Go, TypeScript, or a comparable language.
• Hands-on experience building and deploying applications using large language models, foundation-model APIs, or modern AI development frameworks.
• Experience designing cloud-native applications, APIs, distributed services, or asynchronous workflows.
• Experience with modern source control, automated testing, CI/CD, and collaborative software development practices.
• Experience with production monitoring, observability, and troubleshooting.
• Demonstrated ability to independently navigate ambiguous technical problems and drive solutions from concept through production.
• Strong written and verbal communication skills, including the ability to explain technical decisions to technical and non-technical audiences.

Preferred Qualifications

• Have built and operated production AI agents or agentic applications, using frameworks/SDKs such as LangGraph, OpenAI Agents SDK, AutoGen, or comparable technologies.
• Understand agent architecture patterns including orchestration, routing, tool use, memory, and multi-agent communication.
• Experience with retrieval-augmented generation, embeddings, vector search, or enterprise knowledge integrations.
• Experience with AI observability, evaluation frameworks, model gateways, or LLMOps tooling.
• Understand security considerations specific to AI systems, prompt injection, data access, tool permissions, and agent autonomy.
• Experience building AI solutions in healthcare or another regulated environment.
• Use AI-native development tools (e.g., Claude Code, Codex, Cursor, GitHub Copilot) as a meaningful part of your workflow.
• Experience mentoring engineers or establishing engineering patterns without formal people-management authority.
• Can show production work, open-source contributions, or side projects that demonstrate how you build with AI.
• Hands-on familiarity with technologies and concepts such as RAG (retrieval-augmented generation), LangChain, LangGraph, Pinecone, Hugging Face, semantic search, MCP (Model Context Protocol), A2A (agent-to-agent) communication, and pgvector.