> Markdown version of [/jobs/ext/4262133-ai-engineer](https://www.wearedevelopers.com/jobs/ext/4262133-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Certified Collateral Corporation - **Location:** Chicago, IL, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** LangGraph Framework, A/B Testing, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Command-Line Interface, Code Review, Encodings, Cursor, Software Debugging, Graph Database, Python (Programming Language), PostgreSQL, MongoDB, Neo4j, Search Technologies, Software Engineering, Data Streaming, TypeScript, Microsoft Agent Framework, GitHub Copilot, ReactJS, Large Language Models, Claude Code, Backend, Agentic-AI, Event Driven Architecture, LangSmith, Bicep, Model Context Protocol, Terraform, Microservices, Agent2Agent Protocol - **Published:** October 29, 2026 - **Apply:** https://www.themuse.com/jobs/cccintelligentsolutions/ai-engineer-36e952 ## About the Role * 3+ years of professional software engineering experience, including at least one LLM-powered feature you shipped and then operated in production. * Python and TypeScript: typed, tested, async code in both, and real depth in one. * Evals you have designed for LLM output, plus guardrails you have shipped. * A clear account of how you constrain an agentic coding tool's context and verify its output, from daily use of Claude Code, Cursor, GitHub Copilot, or similar on production work. * Pipelines that blend LLMs with semantic search and classical NLP: structured output, tool calling, embedding-based retrieval, and a case where a classical technique beat an LLM. * Services built on message- or event-driven architectures, including long-running workflows with retries, idempotent handlers, and event-sourced state, plus an LLM or agent step you ran inside one. * Data modeling and development across relational, document, and graph databases such as PostgreSQL, MongoDB, and Neo4j, including when each type fits and the partition-key, consistency, and tenant-isolation calls you made. * Backend services built and operated on Azure or AWS, and exposure to a durable workflow engine such as Temporal or the Durable Task Scheduler. * Technical design documents you have written to get feedback and buy-in from a team before building, and that the team then built to. * Clear writing and discussion for technical and non-technical audiences, and code review you can give as well as take. Even Better if You Have: * React and TypeScript experience, so you can deliver a full-stack feature end to end. Chat, streaming, and citation-heavy AI experiences especially. * Experience with CI/CD pipelines and infrastructure as code such as Terraform or Bicep. * Experience with agent frameworks, LangGraph and the Microsoft Agent Framework among them, and with the ways agents reach their tools: the Model Context Protocol (MCP), the Agent2Agent (A2A) protocol, command-line tools, and microservices. * Experience with LLM observability and evaluation tooling such as LangSmith, Arize, or Braintrust, and with OpenTelemetry-based tracing. * Experience designing A/B tests for AI features, or what-if simulations that replay historical claims through a proposed change before it ships. * Exposure to the insurance, claims, or automotive repair domains. ## Description You will build and ship LLM-powered features and agentic workflows for our customers. These features automate the routine parts of a claim and guide customers on the rest, so they have to earn that trust with measured accuracy, not a convincing demo. Agents run as steps inside deterministic workflows, and privacy and security are design baselines, not review gates. Agentic coding tools are how you move fast. What you add is what they are worst at: system design, security, and hard debugging. You own features end to end., * Ship LLM-powered features and agentic workflows in Python and TypeScript, from a problem scoped with product managers and data scientists through production traffic, and help set the metrics you will be measured against. * Write the evals, guardrails, and feedback loops that make your features trustworthy, and hold them to explicit token and latency budgets. * Use agentic coding tools every day, and own the review of what they produce: catch the correctness, security, and design problems in generated code before you open the pull request. * Maintain and extend the NLP pipelines that turn unstructured claim data into structured data customers can act on, blending semantic search, classical NLP, and LLMs. * Build and operate durable, event-driven services in which agents run as steps inside deterministic workflows, with checkpointed model outputs so a retry does not re-invoke a completed LLM step. * Trace every model call and agent step so a recommendation can be reconstructed and audited, with citations back to the source documents, and wire that tracing into your alerts. * Model and query claims data across relational, document, and graph stores. * Design privacy and security in from the start: tenant isolation, least-privilege access for agents and their tools, and a clear rule for what data may reach a model provider. * Write a technical design document before any significant change, and use it to get feedback and buy-in from the team before you build. * Take part in a shared on-call rotation for the services you build.