AI Engineer

Summary

JD Power powers auto-related decisions through proprietary data, advanced analytics, deep industry expertise, and workflows that connect insight to action. The AI Engineer builds full-stack components for Innovation Crew pilots and prototypes, including backend services, API integrations, data connectors, frontend implementations, and AI agent capabilities. The role also contributes to the Power Agents library, integrates systems with JD Power’s data infrastructure, and develops observable, testable AI solutions.

Responsibilities

• Build full-stack features and components for Innovation Crew pilots under the technical direction of the Principal AI Engineer: backend services, REST APIs, data pipeline connectors, and frontend implementations; writing clean, well-documented code oriented toward handoff to maintaining teams within Product Engineering, Engineering, OEM Solutions, Infrastructure, or Internal Platforms
• Integrate Innovation Crew builds with JD Power's core data infrastructure: Snowflake, internal APIs, cloud platforms, and the API Gateway following published gateway standards and working with Internal Platforms to confirm data access, schema conventions, and connection patterns before building
• Contribute to Power Agents library development: implement new agent patterns under the direction of the Principal AI Engineer, write tests, instrument traces, and document behavior so every module you contribute is auditable, testable, and reusable by the engineering organization
• Contribute to the team's AI agent workforce: build tool integrations, evaluation harnesses, and observability instrumentation that give development agents access to JD Power's systems; implement the baseline evals that let the team verify agent behavior is reliable before relying on it
• Consume and configure frontier model APIs, agentic frameworks (LangGraph, CrewAI, or equivalent), and MCP server integrations as directed; develop hands-on proficiency with the team's AI engineering stack in a production-adjacent environment with real delivery pressure
• Collaborate closely with the AI Experience Engineer to bring interface designs to life: implement frontend components, connect UI layers to backend services, and contribute to the shared component library
• Support intake technical scoping by mapping integration dependencies, spiking on technical unknowns, and estimating effort; participate in Emerging Technology evaluations by building PoC implementations and contributing benchmark findings to Technology Radar drafts

Skills

• • Full-stack engineering fundamentals across backend (REST APIs, cloud-native service patterns) and frontend: solid enough to build a working API, connect it to a data source, and surface it through a functional UI - you do not need a dedicated teammate to do either half
• • Genuine, demonstrable curiosity about AI systems: you have built something with an LLM API, understand what RAG and tool-calling mean in practice, follow frontier model developments, and actively use AI coding tools as part of how you work
• • Clean, well-documented code and a fast-learning orientation: this role pairs closely with principal-level engineers who move quickly; you absorb direction, ask sharp questions, and contribute meaningfully without waiting for complete specifications
• Candidates must be legally authorized to work in the country where employment is offered. JD Power does not provide employment sponsorship for this position
• • Python and/or TypeScript; experience with LangChain, LangGraph, or equivalent agent frameworks; familiarity with Snowflake, BigQuery, or equivalent cloud data platform
• • RAG patterns, vector databases (pgvector, Pinecone, Weaviate), and basic eval harness design, even if you have only done this in a side project, knowing the concepts matters
• • Observability basics: you know the difference between a trace and a log, have seen Langfuse or LangSmith in use, and understand why instrumentation is part of building, not a post-build step
• • JD Power internal platform or API familiarity; existing knowledge of our data schemas or product ecosystem reduces ramp time significantly

Qualifications

Must Haves

• • Full-stack engineering fundamentals across backend (REST APIs, cloud-native service patterns) and frontend: solid enough to build a working API, connect it to a data source, and surface it through a functional UI - you do not need a dedicated teammate to do either half
• • Genuine, demonstrable curiosity about AI systems: you have built something with an LLM API, understand what RAG and tool-calling mean in practice, follow frontier model developments, and actively use AI coding tools as part of how you work
• • Clean, well-documented code and a fast-learning orientation: this role pairs closely with principal-level engineers who move quickly; you absorb direction, ask sharp questions, and contribute meaningfully without waiting for complete specifications
• Candidates must be legally authorized to work in the country where employment is offered. JD Power does not provide employment sponsorship for this position

Nice to Haves

• • Python and/or TypeScript; experience with LangChain, LangGraph, or equivalent agent frameworks; familiarity with Snowflake, BigQuery, or equivalent cloud data platform
• • RAG patterns, vector databases (pgvector, Pinecone, Weaviate), and basic eval harness design, even if you have only done this in a side project, knowing the concepts matters
• • Observability basics: you know the difference between a trace and a log, have seen Langfuse or LangSmith in use, and understand why instrumentation is part of building, not a post-build step
• • JD Power internal platform or API familiarity; existing knowledge of our data schemas or product ecosystem reduces ramp time significantly

Benefits

• As a virtual-first company, we offer flexible work arrangements
• Opportunities for learning and development