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Panasonic Automotive Systems America

Senior AI Engineer

AI Application EngineeringUpdated on Nov 10
Full-timePeachtree City, GA, USA

About the Job

Requirements

  • At least 5 years of software engineering experience, including at least 2 years building LLM, RAG, or agent-based systems that reached production.
  • Strong Python skills and working knowledge of TypeScript or another backend language.
  • Hands-on experience with LLM APIs and open-weight models, function calling, structured outputs, multimodal inputs, and agent frameworks such as LangGraph or Semantic Kernel.
  • Experience with vector or hybrid search and embeddings.
  • Experience building LLM evaluations and observability, such as OpenTelemetry-based tracing, Langfuse, or LangSmith.
  • Experience building and deploying services on AWS, Azure, or GCP using Docker and CI/CD.
  • Working knowledge of secure API design, authentication using OAuth/OIDC, and data privacy.
  • Daily use of AI coding assistants in the candidate's workflow.
  • Experience with MCP or A2A agent interoperability.
  • Experience with Kubernetes, model serving, or fine-tuning, such as vLLM or LoRA.
  • Experience integrating with enterprise applications, identity/SSO, and data platforms.
  • Familiarity with AI governance frameworks such as NIST AI RMF and ISO/IEC 42001.
  • Public technical work or open-source contributions in AI systems.
  • Valid authorization to work in the United States.

Responsibilities

  • Build and ship LLM-powered applications and agents, including orchestration, tool calling, memory, and human-in-the-loop controls.
  • Build RAG pipelines for ingestion, chunking, embeddings, hybrid retrieval, reranking, and retrieval quality measurement.
  • Develop MCP servers and tool integrations connecting agents to enterprise systems and data, with per-tool authorization and OAuth-based agent identity and delegated access.
  • Build evaluation suites, including LLM-as-judge and step-level agent evaluations, tracing, and monitoring for model and agent behavior; use them as release gates and to catch regressions.
  • Tune prompts, context, and model selection for quality, latency, and cost; manage routing across small, frontier, and reasoning models, with fallback, through an LLM gateway.
  • Implement guardrails against prompt injection, data leakage, and unsafe tool actions, with audit logging.
  • Build and maintain APIs and backend services around AI components using containers, CI/CD, and infrastructure as code.
  • Operate shipped systems by monitoring, debugging, and improving AI services in production.
  • Use AI coding assistants and agents to improve delivery speed and share working patterns with peers.
  • Write technical documentation and mentor engineers through code review and pairing.

About the Company

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