AI Engineer (CCC Auto / Agentic)

EvolutionIQ•Chicago, IL

About The Position

CCC Intelligent Solutions Inc. is hiring an AI Engineer to build and ship LLM-powered features and agentic workflows for customers. These features automate routine claim tasks and guide customers through the rest of the process. Agents run as steps within deterministic workflows, with accuracy, privacy, and security treated as core design requirements. The engineer owns features end to end, from scoping with product managers and data scientists through production operation, and uses agentic coding tools while applying system design, security, and debugging expertise. The role includes maintaining and extending NLP pipelines that turn unstructured claim data into structured, actionable information; operating durable event-driven services; and ensuring model calls and agent steps are traceable and auditable. The position participates in a shared on-call rotation. The salary range is $170,000–$195,000, and the position is bonus eligible. CCC is a cloud platform serving businesses across the insurance, repair, automotive, and parts industries. The company describes its culture as ambitious, collaborative, and empathetic, and emphasizes integrity, customer focus, innovation, inclusion and diversity, tenacity, and connection. A video interview is required; interviews are transcribed and retained. Candidates may not use generative AI or automated assistance during interviews unless the interview team explicitly permits it for a specific exercise.

Requirements

At least 3 years of professional software engineering experience, including shipping and operating at least one LLM-powered feature in production.
Typed, tested, asynchronous Python and TypeScript code skills, with deep expertise in at least one of the languages.
Experience designing evaluations for LLM output and shipping guardrails.
Daily production use of Claude Code, Cursor, GitHub Copilot, or a similar agentic coding tool, including the ability to constrain its context and verify its output.
Experience building pipelines that combine LLMs, semantic search, and classical NLP, including structured output, tool calling, embedding-based retrieval, and an example where a classical technique outperformed an LLM.
Experience building services on message- or event-driven architectures, including long-running workflows, retries, idempotent handlers, event-sourced state, and an LLM or agent step within a workflow.
Data modeling and development across relational, document, and graph databases, such as PostgreSQL, MongoDB, and Neo4j, including decisions about database fit, partition keys, consistency, and tenant isolation.
Experience building and operating backend services on Azure or AWS, and exposure to a durable workflow engine such as Temporal or the Durable Task Scheduler.
Experience writing technical design documents that secure team feedback and buy-in before implementation.
Clear communication with technical and non-technical audiences, and ability to give and receive code review.

Nice To Haves

React and TypeScript experience to deliver full-stack features.
Experience with 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, and agent tool-access approaches such as MCP, A2A, command-line tools, and microservices.
Experience with LLM observability and evaluation tools such as LangSmith, Arize, or Braintrust, and OpenTelemetry-based tracing.
Experience designing A/B tests for AI features or what-if simulations that replay historical claims before a proposed change ships.
Exposure to insurance, claims, or automotive repair domains.

Responsibilities

Ship LLM-powered features and agentic workflows in Python and TypeScript, from product and data-science scoping through production, and help define success metrics.
Create evaluations, guardrails, and feedback loops for trustworthy features, and manage explicit token and latency budgets.
Use agentic coding tools and review their output for correctness, security, and design issues before submitting pull requests.
Maintain and extend NLP pipelines that structure unstructured claim data using semantic search, classical NLP, and LLMs.
Build and operate durable, event-driven services with agents as steps in deterministic workflows and checkpointed model outputs to avoid repeating completed LLM steps during retries.
Trace model calls and agent steps, enable reconstruction and auditing of recommendations with source-document citations, and connect tracing to alerts.
Model and query claims data across relational, document, and graph data stores.
Design privacy and security into systems, including tenant isolation, least-privilege agent and tool access, and controls on data sent to model providers.
Write technical design documents for significant changes and obtain team feedback and buy-in before implementation.
Participate in a shared on-call rotation for the services built.