Staff AI/ML Platform Engineer - MLOps & GenAI

Lockheed Martin Corporation; Texas; On-site; USD 121,000 - 224,000; Full time

Benefits offered by this job

401(k) match

Paid time off

Flexible work arrangements

Healthcare benefits

Job summary

Lockheed Martin Corporation is seeking a Staff-level Forward Deployed Engineer for the AI Factory platform in the United States. You will lead the use of the AI Factory platform to solve customer MLOps and GenAI problems, owning discovery, architecture, implementation strategy, deployment, and adoption for high-value AI/ML workflows.

You will work to identify weaknesses in the platform and drive remediation with core engineering teams, ensuring maximum reuse and security.

Qualifications

• 5+ years in software/ platform engineering or MLOps.
• Experience designing, deploying, or operating MLOps pipelines.
• Strong communication with customers and stakeholders.
• Experience with LLM/GenAI workflows and evaluation.
• Lead technical direction through architecture and reviews.

Responsibilities

• Own solution discovery, architecture, deployment strategy, and adoption for AI/ML workflows.
• Identify platform weaknesses and drive remediation with core platform teams.
• Collaborate with customers, platform teams, and program stakeholders.
• Ensure secure, scalable deployment patterns and best practices.

Skills

Staff-level engineer leadership

MLOps experience

GenAI systems

Kubernetes deployment

GPU-backed environments

Customer-facing communication

Solution architecture

Education

B.S. in Computer Science or related field

Tools

Kubernetes

GPU environments

Cloud/on-prem hybrid infra

Job description

Lockheed Martin Corporation is seeking a Staff-level Forward Deployed Engineer for the AI Factory platform in the United States. You will lead the use of the AI Factory platform to solve customer MLOps and GenAI problems, owning discovery, architecture, implementation strategy, deployment, and adoption for high-value AI/ML workflows.

You will work to identify weaknesses in the platform and drive remediation with core engineering teams, ensuring maximum reuse and security.