AI Platform Engineer Sr (API expert)

Lockheed Martin•Fort Worth, CA

1d•Remote

About The Position

The Lockheed Martin Artificial Intelligence Center (LAIC) is seeking a Platform-focused AI Engineer to drive the development of our internal AI Factory. As an AI Platform Engineer, your primary product is the platform itself. You will be responsible for building the robust back-end services, intuitive front-end interfaces, and seamless API contracts that enable AI/ML practitioners to deploy GenAI and MLOps workloads at scale. We are looking for a "Force Multiplier"—an engineer who not only builds AI platforms but effectively leverages AI tools and autonomous agents to accelerate their own development lifecycle and improve code quality.

Requirements

Bachelor's degree in Computer Science, Engineering, or related technical field, or equivalent practical experience
Experience building and maintaining production backend services in a modern language (e.g., Python, Go, Java, C#)
Experience designing, implementing, and operating REST APIs in production
Experience deploying and operating containerized services in Kubernetes
Experience collaborating across teams to deliver software from design through production
Must be a US Citizen.

Nice To Haves

Experience with GenAI or ML platform engineering: model serving, evaluation infrastructure, prompt/RAG lifecycle or MLOps tooling
Full-stack experience with modern frontend frameworks (React, Angular, or Next.js) for UI contributions
Backend framework experience with FastAPI, Flask, Gin, or Spring Boot
Working knowledge of OAuth2/OIDC authentication patterns
Experience with API contracts and documentation standards (OpenAPI/Swagger)
Track record using AI coding assistants (Claude Code, Codex, etc.) to ship production work
Experience operating multi-tenant platforms or internal developer tools
Exposure to infrastructure-as-code and GitOps workflows

Responsibilities

API-First Development: Design, implement, and maintain high-performance RESTful APIs that serve as the backbone for AI model orchestration and resource management.
Full-Stack Ownership: Develop end-to-end features, from building responsive React/Next.js front-ends to engineering scalable Python/Go back-end services.
Platform Design & Governance: Collaborate on the design of AI Platform components, ensuring that internal services are modular, discoverable, and capable of supporting LLM, RAG, and Agentic workflows.
AI-Augmented Engineering: Proactively integrate AI coding assistants (e.g., Open Code, Cline, Roo, Cursor) and custom LLM agents into your daily workflow to automate boilerplate, generate tests, and conduct initial code reviews.
Service Integration: Bridge the gap between low-level Kubernetes infrastructure and high-level user interfaces by creating middleware that abstracts complexity for the end user.
System Architecture: Optimize the data flow between front-end dashboards, back-end metadata stores (PostgreSQL/Redis), and the underlying GPU-accelerated compute clusters.