CCC Intelligent Solutions Inc. is hiring an AI Engineer in Chicago, IL (onsite) to build and deploy LLM-powered features and agentic workflows for automating claim tasks, with a focus on measured accuracy, privacy, and security.
Role Overview
You will own LLM-enabled capabilities end to end, including evaluation and guardrails, durable event-driven services, tracing and auditing, and NLP pipelines that convert unstructured claim information into structured outputs for customer use.
Key Responsibilities
- Ship LLM-powered features and agentic workflows in Python and TypeScript, partnering with product managers and data scientists from problem scoping through production traffic, and help define the metrics used to evaluate your work.
- Develop evals, guardrails, and feedback loops to improve trustworthiness, with explicit token and latency budgets.
- Use agentic coding tools daily and review outputs you generate: identify correctness, security, and design issues in generated code before submitting pull requests.
- Maintain and extend NLP pipelines that transform unstructured claim data into structured data, combining semantic search, classical NLP, and LLM approaches.
- Build and operate durable, event-driven services where agents run as steps inside deterministic workflows, using checkpointed model outputs so retries do not re-invoke completed LLM steps.
- Trace every model call and agent step so recommendations can be reconstructed and audited, including citations to source documents, and integrate tracing into alerts.
- Model and query claims data across relational, document, and graph stores.
- Design privacy and security from the start, including tenant isolation, least-privilege access for agents and tools, and clear rules for what personal and medical data may be sent to a model provider.
- Produce technical design documents before significant changes to obtain team feedback and buy-in, and use that documentation to guide implementation.
- Participate in a shared on-call rotation for the services you build.
Required Qualifications
- 3+ years of professional software engineering experience, including at least one LLM-powered feature you shipped and operated in production.
- Strong Python and TypeScript experience, including typed, tested, and async code, with real depth in at least one.
- Experience designing evals for LLM output and shipping guardrails.
- Ability to explain how you constrain agentic coding tool context and verify outputs, based on daily use of Claude Code, Cursor, GitHub Copilot, or similar.
- Experience building pipelines that blend LLMs with semantic search and classical NLP, including structured output, tool calling, embedding-based retrieval, and a case where a classical technique outperformed an LLM.
- Experience with message- or event-driven services, including long-running workflows with retries, idempotent handlers, event-sourced state, and running an LLM or agent step inside such workflows.
- Backend data modeling experience with stores such as PostgreSQL, MongoDB, Neo4j, SQL Server, and Cosmos DB, with real depth in one relational and one non-relational store, plus familiarity with tenant isolation and regulated personal data.
- Experience building and operating backend services on Azure or AWS, with familiarity with durable workflow engines such as Temporal or the Durable Task Scheduler.
- Demonstrated ability to write technical design documents for team feedback and buy-in before implementation.
- Clear writing and discussion skills for both technical and non-technical audiences, supported by effective code review collaboration.
Technologies
Python, TypeScript, Claude Code, Cursor, GitHub Copilot, semantic search, classical NLP, LLMs, embedding-based retrieval, structured output, tool calling, message- or event-driven architectures, long-running workflows, idempotent handlers, event-sourced state, PostgreSQL, MongoDB, Neo4j, SQL Server, Cosmos DB, tenant isolation, Azure, AWS, Temporal, Durable Task Scheduler, React, Terraform, Bicep, LangGraph, Microsoft Agent Framework, Model Context Protocol (MCP), Agent2Agent (A2A) protocol, microservices, LangSmith, Arize, Braintrust, OpenTelemetry, CI/CD, A/B tests
Benefits
- 401K Match
- Paid time off
- Annual Incentive Plan Performance Bonus
- Comprehensive health insurance
- Adoption Assistance
- Tuition Reimbursement
- Wellness Programs
- Stock Purchase Plan options
- Employee Resource Groups
Even Better If You Have
- React and TypeScript experience to deliver full-stack features end to end, including chat, streaming, and citation-heavy AI experiences.
- Experience with CI/CD pipelines and infrastructure as code such as Terraform or Bicep.
- Experience with agent frameworks, including LangGraph and the Microsoft Agent Framework, plus knowledge of how agents reach tools using MCP, A2A, command-line tools, and microservices.
- Experience with LLM observability and evaluation tooling such as LangSmith, Arize, or Braintrust, and tracing with OpenTelemetry.
- Experience designing A/B tests for AI features, or what-if simulations that replay historical claims through proposed changes.
- Exposure to the insurance, claims, or automotive repair domains.
What Success Looks Like
- Within the first six months, ship an LLM-powered feature, ensure evals run in CI, and identify the metric moved.
- When prompt or model changes introduce regressions, the evals you wrote detect issues before customers are impacted.
- Own work from concept to production with light direction, asking appropriate questions early.
- Build services that remain reliable through retries, partial failures, and model migrations without customer-visible incidents.
- Use production traces to identify which step and model call failed, supporting audit needs with the same reconstructed evidence.
- Have privacy and security reviews confirm your features meet the intended design boundaries.
- Demonstrate engineering craft so your patterns are adopted by other engineers.
- Engage in feature scoping discussions productively and debate technical positions effectively.
- Work comfortably in an environment where measured outcomes matter more than arguments.
Interview Policy & Privacy Notice
- A video interview is required.
- Video interviews are transcribed.
- Transcriptions are retained and may be reviewed by CCC and recruiters.
- Candidates may not use generative AI or automated assistance during interviews unless explicitly permitted by the interview team for a specific exercise.
- CCC’s Job Applicant Privacy Notice is available HERE.
Compensation and Location
- Location: Chicago, IL (onsite)
- Salary: USD 119,030 - 150,000 per year
- Minimum Experience: 3 years
