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. What’s In It For You From onsite to remote, we offer flexible work schedules to comprehensive benefits investing in your future and security, Learn more about Lockheed Martin’s comprehensive benefits package here. Do you want to be part of a company culture that empowers employees to think big, lead with a growth mindset, and make the impossible a reality? We provide the resources and give you the flexibility to enable inspiration and focus -if you have the passion and courage to dream big, work hard, and have fun doing what you love then we want to build a better tomorrow with you. #LMLAIC

Requirements

Education: Bachelor’s Degree in Engineering, Computer Science, or related technical discipline.
Full-Stack Development: Proven experience with both Back-End frameworks (FastAPI, Flask, Gin, or Spring Boot) and Front-End frameworks (React, Angular, or Next.js).
API Mastery: Advanced knowledge of RESTful API design principles, including versioning, authentication (OAuth2/OIDC), and documentation (OpenAPI/Swagger).
AI Tooling Proficiency: Demonstrated ability to use AI-assisted development tools and agents to increase engineering velocity and maintain high-quality codebases.
Cloud-Native Foundation: Hands-on experience deploying and managing containerized services within Kubernetes.
US Citizenship: Must be a US Citizen.

Nice To Haves

Architectural Strategic Thinking: A growing ability to apply strategic thinking to complex platform problems, focusing on long-term scalability and Cloud Native software development practices.
High-Throughput Distributed Systems: Experience building and optimizing large-scale distributed systems and Platform services that support intensive compute workloads.
Infrastructure-Intensive Execution: Proven results in executing on infrastructure-intensive programs, specifically within Kubernetes and public cloud environments (AWS, GCP, Azure).
Configuration & Package Management: Expert-level familiarity with Helm or Kustomize to manage complex service deployments.
Creative and Resourceful Problem-Solving: A passionate, can-do attitude with the resourcefulness to navigate an industry that constantly changes.
Empathetic DevEx Design: Deep empathy for teammates and users, with a desire to make their workflows frictionless, efficient, and useful.
Virtual Collaboration: Proven experience collaborating with virtual, cross-functional teams to deliver mission-critical software.
Advanced MLOps Maturity: Proven experience in operationalizing the ML lifecycle, specifically in building and maintaining automated training and inference pipelines. Knowledge of Model Registry management, lineage tracking, and the integration of MLOps tools like MLflow, Kubeflow, or Flyte into a unified platform.
Strategic AI/ML Lifecycle Expertise: Understanding of the end-to-end AI/ML lifecycle, with experience optimizing inference services, vector database integration (Pinecone, Milvus, or Weaviate), and RAG (Retrieval-Augmented Generation) architectures.
Agentic System Design: Familiarity with designing or deploying agentic workflows and orchestration frameworks (such as LangChain, CrewAI, or AutoGen) to solve non-linear engineering or business problems.

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.