Note: The job is a remote job and is open to candidates in USA. Autodata Solutions Company is hiring an Applied AI Engineer for its Launch Lab team. The role delivers full-stack AI-powered applications from proof-of-concept handoff through production deployment and operational handoff, while building evaluation and observability capabilities for trustworthy AI applications.
Responsibilities
- Lead application architecture and implementation across active Launch Lab delivery tracks: build AI-powered full-stack applications on 2-to-4-week sprint cycles, from wireframe through polished production-quality interface; support the frontend and UX quality of all Launch Lab outputs including adoption of a shared component library and design system
- Receive, interpret, and execute PoC Blueprint Handoffs from the Innovation Crew: translate validated architectures (Power Agents integrations, vector DB hooks, agent workflow patterns) into production applications with real users and measurable outcomes; build evaluation harnesses and observability instrumentation that make every application's AI behavior inspectable in production
- Build on the Innovation Arena's layered architecture: consume Power Agents and Agent Skills Registry modules; surface Intelligence layer capabilities (Data Product Layer, Industry Intelligence Agents, Domain Context Workflows) through polished user-facing applications; apply QE Agent Capabilities and Infrastructure and Tooling from the Quality and Delivery layer to maintain delivery standards
- Integrate Launch Lab outputs into JD Power's core data infrastructure: Snowflake, internal APIs, and the API Gateway - following published gateway standards and working closely with Internal Platforms to confirm data access and connection patterns; ensure AppSec, Data Governance, PII handling, and OEM data requirements are embedded in design, not bolted on after the fact
- Participate in Gate 2 technical scoping: scope new requests, estimate delivery effort, identify integration dependencies, flag Power Agents applicability, and define measurable success criteria including the evaluation criteria and cost budgets for each AI feature before building begins
- Own each Launch Lab output through to live: deploy in its target environment, confirm it is operational and available for users, then execute a clean handoff to the maintaining team within Product Engineering, Engineering, OEM Solutions, Infrastructure, or Internal Platforms - leaving behind documentation the receiving team can actually use
- Contribute application-layer technology evaluations to the Emerging Technology Radar; propose directional Architecture Decision Records for patterns and technology choices made during active delivery tracks; conduct lightweight user testing with business unit partners
Skills
- A portfolio of shipped, production AI applications: you have built and deployed real things real users depend on, can walk through what failed in production and how you fixed it, and treat "it worked in the demo" as the beginning of the work - not the end
- Full-stack engineering depth: frontend proficiency with the ability to produce polished production-quality interfaces at speed; backend depth across REST and/or GraphQL, authentication patterns, and cloud-native service architecture
- Hands-on experience with agentic frameworks or LLM APIs (LangChain, LlamaIndex, Anthropic, OpenAI, or equivalent), RAG patterns, streaming responses, and building interfaces that make AI behavior feel intuitive rather than opaque
- JD Power internal platform, data infrastructure, or product ecosystem familiarity - institutional knowledge compresses your ramp and your day-one impact 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
- React preferred
- Eval harness design and LLM observability (Langfuse, LangSmith, Braintrust, or equivalent): you have built evaluation frameworks for AI features and can define what "quality" means for a non-deterministic system before writing a line of production code
- Multi-model routing and cost optimization (LiteLLM, Portkey, or equivalent); experience managing inference cost as a first-class engineering constraint
- Data platform experience (Snowflake, BigQuery, or equivalent) and security basics for AI applications (PII handling, secrets management, prompt injection defense, API audit logging)
- Strong product intuition: you ask "what does the user actually need?" before choosing a model; you write design docs, run your own postmortems, and can explain a model failure to a non-technical stakeholder without panic
Benefits
- As a virtual-first company, we offer flexible work arrangements
- Opportunities for learning and development
Company Overview