Staff-level Forward Deployed Engineer

Serves as a Staff-level Forward Deployed Engineer for AI Factory (an internal AI/ML Ops Platform), leading the use of Lockheed Martin's AI Factory platform to solve customer MLOps and GenAI problems. Owns customer solution discovery, architecture, implementation strategy, deployment, and adoption for high-value AI/ML workflows while ensuring the platform is used effectively, consistently, and with maximum reuse. Uses this deployment work to identify weaknesses in AI Factory's foundational products and platform, such as design flaws, performance gaps, reliability risks, and integration friction, and drives actionable remediation suggestions back to core platform engineering team.

Core Responsibilities
Lead customer-facing technical discovery to translate mission, user, and program needs into deployable AI Factory solutions spanning MLOps pipelines, GenAI applications, and agentic workflows.
Own end-to-end delivery across solution design, integration planning, deployment strategy, rollout, and adoption in close coordination with customer stakeholders and internal platform teams.
Design and oversee deployment of Kubernetes-based MLOps and agentic pipelines across cloud and on-prem GPU-backed environments, including data processing workflows, model training pipelines, and GenAI application backends.
Act as a senior advocate for AI Factory's MLOps and GenAI platforms by guiding customers toward the best use of platform capabilities before bespoke solutions are introduced.
Discover design flaws, performance gaps, reliability risks, and integration friction in foundational products during deployment work, and translate those findings into actionable design change proposals for AI Factory product and engineering teams.
Provide technical direction to other FDEs through design reviews, implementation guidance, deployment standards, and reusable playbooks or reference architectures.
Stay hands-on on the highest-leverage technical work, including debugging deployment blockers, shaping interfaces, and guiding integrations across Kubernetes, data, model, and security boundaries.
Embed responsible AI, security, traceability, and operational guardrails into deployed solutions so they are supportable in production and defensible in regulated environments.
Maintain clear documentation and versioned artifacts to support certification, auditability, and long-term operational support in regulated environments.
Operates with a mixed model with remote work and direct support in customer environments. Success in the role requires balancing customer outcomes, strong platform advocacy, and long-term maintainability of the AI Factory ecosystem.

#LMLAIC 5+ years of related experience in software engineering, platform engineering, MLOps, ML infrastructure, and/or GenAI systems.
Strong communication skills and the ability to work directly with customers, platform teams, and program stakeholders.
Demonstrated experience delivering production AI/ML systems or platform capabilities with ambiguous requirements and multiple stakeholders.
Experience designing, deploying, or operating MLOps pipelines, model deployment workflows, data and artifact lineage, or GenAI application backends.
Demonstrated ability to lead technical direction for other engineers through architecture, decomposition, code review, or deployment standards.
Hands-on familiarity with Kubernetes-based deployment patterns, GPU-backed environments, and secure software delivery practices.
U.S. citizenship and ability to obtain and maintain a Secret or Top Secret clearance. B.S. (or higher) in Computer Science, Electrical / Aerospace Engineering, Applied Mathematics, or related field.
Experience with enterprise AI/ML platforms such as Amazon SageMaker, Databricks Mosaic AI, Google Cloud Vertex AI / Gemini Enterprise Agent Platform, Microsoft Foundry, Domino Data Lab, Dataiku, IBM watsonx, or DataRobot.
Experience with LLM and GenAI systems, evaluation workflows, prompt or context engineering, or agentic application patterns.
Familiarity with Python and at least one backend or systems language such as Go or Rust.
Experience working across cloud and on-prem GPU environments.
Ability to communicate technical tradeoffs clearly to senior stakeholders while maintaining delivery momentum across customer and internal teams.