Basic Qualifications

3+ years of hands-on experience in software engineering, platform engineering, MLOps, ML infrastructure, or GenAI systems.
Demonstrated experience owning delivery of production AI/ML systems, platform capabilities, or deployment workstreams across ambiguous requirements and multiple stakeholders.
Experience designing, deploying, or operating Kubernetes-based MLOps pipelines, model training and inference workflows, or data-processing systems in GPU-backed environments.
Demonstrated ability to debug complex deployment issues across infrastructure, containers, networking, security controls, and application runtime behavior.
Strong written and verbal communication skills with customer engineering teams, internal product groups, and peer engineers.
U.S. citizenship and ability to obtain and maintain a Secret or Top Secret clearance.

Job Description

The Senior Forward‑Deployed Engineer (FDE) works as a hands‑on technical specialist for Lockheed Martin’s AI Factory platform – the internal AI/ML‑Ops foundation that backs our users’ MLOps, GenAI, and agentic workflows. In this role you will partner with senior staff and end users to translate solution concepts into working deployments, implement and tune Kubernetes‑based pipelines, and platform‑level improvement opportunities.

Own assigned customer deployment workstreams end to end, from technical discovery and solution shaping through implementation, rollout, and handoff to sustained use.
Translate customer and user requirements into deployable AI Factory solutions spanning MLOps pipelines, GenAI backends, data-processing workflows, and agentic applications.
Design, deploy, configure, and maintain Kubernetes-based MLOps pipelines across AWS, Azure, GCP, and on-prem GPU-backed environments, including training, inference, and data-processing workflows.
Adapt platform architecture and integration plans to customer-specific infrastructure, security constraints, runtime requirements, and operational standards.
Act as a technical advocate for AI Factory during implementation by steering customers toward out-of-the-box platform capabilities before custom work is introduced.
Capture platform bugs, performance gaps, reliability risks, and integration friction discovered during deployments, and document actionable remediation suggestions for AI Factory product and engineering teams.
Turn repeated deployment pain points into reusable templates, guardrails, configuration patterns, or implementation guidance that improve future delivery.
Embed traceability, auditability, security, and responsible AI guardrails, including model lineage, data provenance, and access controls, into deployed solutions for regulated environments.
Produce clear, versioned deployment artifacts and operational documentation that support certification, audit readiness, and reliable production support.
Own design decisions for assigned workstreams and contribute substantive feedback in design and code reviews.
Mentor less-experienced engineers on deployment best practices such as Kubernetes debugging, CI/CD for ML, and operating production AI systems.
Evaluate emerging MLOps, GenAI, and agentic capabilities for fit with customer delivery needs and Lockheed Martin's AI Factory platform direction.

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

Desired Skills

B.S. (or higher) in Computer Science, Electrical or Aerospace Engineering, Applied Mathematics, or a related discipline.
Working knowledge of platforms such as Amazon SageMaker, Google Vertex AI, Azure ML, Databricks Mosaic AI, IBM watsonx, Dataiku, Domino, or DataRobot.
Familiarity with LLM deployment, prompt engineering, retrieval-augmented generation, or agentic application patterns.
Proficiency in Python plus at least one systems or backend language such as Go, Rust, Java, or C++.
Experience navigating both public-cloud and on-prem GPU-accelerated environments.
Understanding of model and data provenance, bias mitigation, and compliance documentation.

Other Important Information

By applying to this job, you are expressing interest in this position and could be considered for other career opportunities where similar skills and requirements have been identified as a match. Should this match be identified you may be contacted for this and future openings.

Ability to work remotely

Full-time Remote Telework: The employee selected for this position will work remotely full time at a location other than a Lockheed Martin designated office/job site. Employees may travel to a Lockheed Martin office for periodic meetings.