A/AI Machine Learning Engineering Senior

Team Overview

The Rotary Mission Systems (RMS) AI Hub team, within Lockheed Martin's Data & AI Enablement (D&AE) organization, creates tailored, production-grade AI solutions that enable the functions and lines of business across RMS. The team's core capabilities span descriptive data analysis, predictive machine learning, and cognitive AI products - including computer vision, natural language processing, and generative AI. In this role, you will partner with internal stakeholders to translate complex requirements into end-to-end machine-learning pipelines, integrate large-language-model (LLM) capabilities, and drive innovations that accelerate AI value realization within the business area. Your expertise in Python, the data-science lifecycle, deep-learning fundamentals, and emerging AI technologies will empower you to deliver high-impact solutions that generate measurable value for RMS and Lockheed Martin.

Key Responsibilities

• Translate business problems into AI-driven solution architectures.
• Build, fine-tune, and integrate LLMs and complementary AI models into new and existing RMS applications.
• Create test plans, conduct integration testing, and deploy solutions using CI/CD pipelines.
• Document designs and collaborate with change management to ensure smooth user adoption.
• Monitor deployed solutions and drive continuous improvement to keep pace with evolving business needs.

By delivering robust, production-grade AI pipelines and intuitive interfaces, you will enhance efficiency across RMS functions and empower teams to better support Lockheed Martin's customers.

Standard Job Description For A/AI Machine Learning Engineering Senior

Responsible for developing, integrating, and deploying autonomy and artificial intelligence algorithms for mission systems, supporting the technology development life cycle from requirements generation through development, integration, and testing, as well as research in some organizations.Develops, integrates, and implements algorithms to enable perception, motion/mission planning, controls, etc. functionality in LM products and platforms; Translates requirements and applies requirements to development code, integrating autonomy, AI or machine learning algorithms to LM products and platforms; Determines software methods to best acquire and execute knowledge; Implements algorithms into software to train systems to recognize patterns and perform specific functions; Responsible for various phases of developing and maintaining autonomy software from requirements generation, software design and development to integration, testing, troubleshooting and debugging, and implementation; Review test outcomes, conducts troubleshooting, and works to debug issues; Develops human-machine interface scenarios, breaking missions into tasks; Documents interface requirements and implements human-machine interfaces

• Bachelor's degree in Computer Science, Information Technology, Engineering, AI/ML, or a related discipline.
• Proficiency in Python - write clean, well-documented code and explain your logic.
• Knowledge of the data-science lifecycle, big-data concepts, deep-learning fundamentals, and reinforcement-learning ideas.
• Demonstrated success with Generative AI capabilities such as large-language models (LLMs)
• Demonstrated initiative and research mindset - comfortable navigating documentation, exploring new tools, and proposing ideas with minimal supervision.
• Strong problem-solving and communication skills; able to work with internal customers to gather requirements and provide clear status updates.
• Understanding of RESTful APIs and ability to integrate them into data pipelines.

• Experience with LLM frameworks or APIs (e.g., LangChain, OpenAI, Cohere).
• Exposure to MLOps fundamentals: CI/CD pipelines, model versioning, monitoring, Docker/Kubernetes.
• Knowledge-graph or ontology work (SPARQL, embeddings, graph-based feature engineering).
• Familiarity with cloud platforms (AWS, Azure, GCP) and OpenShift.
• Ability to quickly absorb new tools, applications, or approaches from documentation or public repositories and contribute ideas to the team.
• Research-oriented mindset - conducting industry/academic research, networking with peers, and surfacing innovative strategies.
• Breadth of interest or experience with diverse data-application domains (structured, unstructured, time-series, sensor, etc.).
• Experience with Git fundamentals (branching, merging, pull-requests, basic workflow conventions).
• Basic familiarity with Docker (or comparable container technology).