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Panasonic Automotive Systems America

AI Architect

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

About the Job

Requirements

  • Seven or more years of software engineering experience, including two or more years shipping LLM, RAG, or agent-based systems to 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, and agent frameworks such as LangGraph or Semantic Kernel.
  • Experience with vector or hybrid search and embeddings.
  • Experience with MCP and tool-integration design, and familiarity with A2A or similar agent interoperability standards.
  • Experience building LLM evaluations and observability, such as OpenTelemetry-based tracing, Langfuse, or LangSmith.
  • Cloud deployment experience on AWS, Azure, or GCP, with Docker, Kubernetes, and secure API design using OAuth/OIDC.
  • Daily use of AI coding assistants and agents in an engineering workflow.
  • Experience delivering enterprise solutions by integrating enterprise applications, SSO/identity, and data platforms under security and architecture review.
  • Ability to communicate design trade-offs clearly in writing and in person.

Responsibilities

  • Decompose ambiguous business problems into capabilities, information, behaviors, system boundaries, interactions, dependencies, constraints, and quality attributes, and translate them into coherent end-to-end solution architectures spanning business, application, data, integration, AI, security, infrastructure, and operations.
  • Apply architecture principles, patterns, quality attributes, and structured decision-making to solution design; evaluate alternatives based on coupling, scalability, reliability, security, interoperability, maintainability, extensibility, performance, cost, and operational complexity; and articulate architectural decisions, alternatives, and accepted trade-offs.
  • Challenge assumptions and move beyond prescribed vendor patterns or technology-first solutions; explore multiple approaches, reason from first principles, and develop innovative yet pragmatic architectures for complex or previously unsolved problems; decide when to use AI, deterministic technologies, or both in resilient enterprise solutions.
  • Work with business and technical stakeholders to turn use cases into end-to-end enterprise AI solutions, including requirements, solution design, build-versus-buy and platform choices, and delivery roadmaps.
  • Integrate AI with enterprise systems and data, including ERP, identity and SSO, data platforms, APIs, and legacy applications, within existing architecture, security, and compliance standards.
  • Design and build single- and multi-agent systems, including orchestration, tool use, memory, and human-in-the-loop controls.
  • Build RAG and context-engineering pipelines, including retrieval quality, long-running agent memory, and token and latency management.
  • Build MCP servers and agent integrations that expose enterprise tools and data under governed, least-privilege access.
  • Run LLM access through a gateway with model routing, fallback, quotas, and cost and usage tracking.
  • Build evaluation, tracing, and monitoring for LLM and agent behavior, including regression tests and release gates.
  • Secure agents against prompt injection, data leakage, and over-permissioned tools, and maintain audit trails of agent actions.
  • Deploy AI services with containers, CI/CD, and infrastructure as code, and troubleshoot them in production.
  • Support rollout of AI solutions across the enterprise through pilots, user adoption, operational handoff, and feedback loops.
  • Publish reference architectures and working templates, and review designs and code with partner teams.

Desired Qualifications

  • Model serving or fine-tuning experience, such as vLLM or LoRA, including edge or on-device inference.
  • Working knowledge of AI governance frameworks such as NIST AI RMF, ISO/IEC 42001, or the EU AI Act.
  • Automotive, embedded, or manufacturing software background.
  • Vendor-neutral certifications or public technical work in AI systems.

About the Company

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