AI Engineer

CCC Intelligent Solutions

. Build and ship LLM-powered features and agentic workflows for insurance customers .

Posted 9/18/2026full-timeChicago • Illinois • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and shipping LLM-powered features, automating claim tasks, and maintaining NLP pipelines. Proficient in data modeling across relational and non-relational stores while ensuring compliance with personal data regulations.

Highest-signal resume keywords

LLM-Powered Feature DevelopmentPython and TypeScript ProficiencyData Modeling in Relational and Non-Relational StoresExperience with Event-Driven ArchitecturesTechnical Design Documentation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM Output EvaluationsNLP Pipeline MaintenanceData ModelingEvent-Driven ArchitectureAsynchronous ProgrammingCode ReviewAutomated TestingSecurity Risk AssessmentSemantic Search IntegrationIdempotent Handling

Soft Skills

Clear CommunicationTeam CollaborationFeedback Reception

Tools & Technologies

Claude CodeCursorGitHub CopilotAzureAWSTemporalDurable Task SchedulerTerraformBicepLangGraph

Industry Keywords

InsuranceClaimsAutomotive RepairPersonal Data RegulationsTenant Isolation

Tech Stack

Tools & technologies

AWSAzureMicroservicesMongoDBNeo4jPostgresPythonReactSQLTerraformTypeScript

About the role

Key responsibilities & impact

Build and ship LLM-powered features and agentic workflows for insurance customers
Automate routine claim tasks and guide customers through remaining steps
Scope problems with product managers and data scientists, define metrics, and own features through production
Write evaluations, guardrails, and feedback loops with explicit token and latency budgets
Review AI-generated code for correctness, security, and design issues
Maintain and extend NLP pipelines transforming unstructured claim data into structured actionable data
Build and operate durable event-driven services with deterministic workflows, retries, checkpointed model outputs, and idempotent handling
Trace model calls and agent steps with source citations and alerting for reconstruction and auditability
Model and query claims data across relational, document, and graph stores
Design tenant isolation, least-privilege agent access, and controls for personal and medical data
Write technical design documents and obtain team feedback before implementation
Participate in a shared on-call rotation
Deliver an LLM-powered feature with CI evaluations within the first six months
Diagnose regressions, production failures, privacy issues, and security risks

Requirements

What you’ll need

3+ years of professional software engineering experience, including at least one LLM-powered feature shipped and operated in production
Python and TypeScript: typed, tested, async code in both, with real depth in one
Experience designing LLM output evaluations and shipping guardrails
Daily production use of Claude Code, Cursor, GitHub Copilot, or similar agentic coding tools; ability to constrain context and verify output
Experience blending 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 architectures, long-running workflows, retries, idempotent handlers, event-sourced state, and LLM/agent steps
Data modeling in relational and non-relational stores such as PostgreSQL, MongoDB, Neo4j, SQL Server, and Cosmos DB; depth in one relational and one non-relational store
Familiarity with tenant isolation and regulated personal data
Backend services operated on Azure or AWS
Exposure to Temporal or Durable Task Scheduler
Experience writing technical design documents and obtaining team feedback and buy-in
Clear writing and discussion for technical and non-technical audiences; ability to give and receive code review
Video interview required
Preferred: React and TypeScript; CI/CD and Terraform or Bicep; LangGraph or Microsoft Agent Framework; MCP, A2A, command-line tools, or microservices; LangSmith, Arize, Braintrust, or OpenTelemetry; A/B testing or what-if simulations; insurance, claims, or automotive repair experience

Benefits

Comp & perks

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
Bonus and/or commission eligibility