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# AI Engineer

- **Company:** Jd Power
- **Location:** United States (Remote available)
- **Salary:** $115,000.0 - $130,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Data Security, Database Schema, Design of User Interfaces, Python (Programming Language), TypeScript, Cloud Platform System, Large Language Models, Snowflake, Multi-Agent Systems, Backend, Material UI, Core Data, Operational Systems, Front End Software Development, Virtual Agents, Api Gateway, Restful APIs, Data Pipelines, Api Management
- **Published:** September 12, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/18280458?backUrl=%2Fcareer%2F18280458%2FAi-Engineer-Michigan

## About the Role

* 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.

Also Valued

 * 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., Candidates must be legally authorized to work in the country where employment is offered. JD Power does not provide employment sponsorship for this position.

## Description

You build the full-stack components that power Innovation Crew pilots and prototypes. Under the technical direction of the Principal AI Engineer, in close collaboration with the AI Experience Engineer, you write the backend services, API integrations, data connectors, and frontend implementations that turn AI architectures into working systems. This role has a high ceiling and a fast runway: you will be working alongside engineers at the frontier of AI application development, with hands-on exposure to frontier model APIs, agentic frameworks, Power Agents library development, and JD Power's core data infrastructure. You arrive with strong engineering fundamentals and a genuine appetite for AI systems. You gain deep, production-proven agentic engineering experience. In your first 30 days, you will have your first feature shipped inside a PoC sprint and the codebase oriented. By day 60, you will have your first Power Agents contribution reviewed and merged. By day 90, you will have full
implementation ownership of at least one PoC component.

How we Build Agents:

The Innovation Crew builds alongside its own AI agent workforce. As AI Engineer, you will contribute to building and maintaining those agents: writing the tool integrations, evaluation harnesses, and API connectors that give agents access to JD Power's data and systems. You learn to build for observability from day one - every agent pattern you ship is instrumented, testable, and inspectable. The bar here is not "it worked in the demo" but "it behaves predictably, and we can prove it." When a pattern proves reliable, you contribute it to the Power Agents library, where 400+ engineers & knowledge workers can build on it.

The Impact You Will Have in This Role:

Innovation Crew pilots only get built because the engineering work that underlies them gets done cleanly and quickly. You are the execution engine that turns architectural direction into working code. You build backend services that connect to Snowflake, API integrations that wire agent outputs to JD Power's core platforms, frontend components the AI Experience Engineer builds on. The pilots you help build will be handed off as operational systems to maintaining teams within Product Engineering, Engineering, OEM Solutions, Infrastructure, and Internal Platforms. You will contribute directly to the Power Agents library, meaning code you write will be used by engineers & knowledge workers across JD Power.

What You'll Be Doing in This Role:

 * 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.


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