AI Machine Learning Engineer Sr. (mid-career)

Lockheed Martin is seeking a Senior AI Machine Learning Engineer to join the Enterprise Interoperability Framework team. This role involves designing, developing, and supporting software solutions that enable interoperability between enterprise systems and AI-enabled workflows.

Responsibilities:

• Designing and developing full stack software solutions using modern front-end, back-end, database, and API technologies
• Building integrations between enterprise systems through REST APIs, event-driven patterns, middleware (e.g. eQube, Quarkus, AMQ), ETL/data pipelines, and automation frameworks
• Developing AI-enabled capabilities that incorporate large language models, AI services, prompt engineering, model APIs, semantic search, document analysis, or intelligent workflow automation
• Creating and maintaining APIs and services that support interoperability, data exchange, system synchronization, and workflow orchestration
• Implementing secure software engineering practices aligned with enterprise cybersecurity, DevSecOps, and data governance requirements
• Collaborating with cross-functional teams to translate business needs into technical solutions and deliver capabilities in an agile environment
• Troubleshooting complex integration issues involving data mappings, authentication, API behavior, system dependencies, performance, and reliability
• Supporting CI/CD pipelines and deployment activities for cloud, containerized, or enterprise-hosted applications
• Providing technical leadership within the team, including solution design input, code reviews, mentoring, and guidance on engineering best practices

Requirements:

• Experience developing software using one or more modern programming languages, such as JavaScript, TypeScript, Python, Java, C#, or similar
• Experience developing full stack applications, including user interface, back-end service, API, and database components
• Experience designing, developing, or consuming REST APIs or similar integration interfaces
• Experience with Git-based software configuration management tools, such as GitLab, GitHub, Bitbucket, or similar
• Experience working in an Agile software development environment
• Experience developing AI-enabled applications, including integration with large language models, AI APIs, retrieval-augmented generation, semantic search, embeddings, prompt engineering, or intelligent workflow automation
• Experience building enterprise system integrations using REST APIs, message queues, event-driven architectures, middleware, ETL/data pipelines, or workflow automation
• Experience with integration or middleware technologies, such as eQube, Quarkus, AMQ, Kafka, MuleSoft, Camel, or similar
• Experience with DevSecOps practices, including CI/CD pipelines, automated testing, static code analysis, dependency scanning, container scanning, secrets management, or infrastructure as code
• Experience with cloud or container platforms, such as Docker, Kubernetes, OpenShift, AWS, Azure, or similar
• Experience with enterprise software development tools, such as GitLab, Jira, Confluence, Artifactory, Jenkins, or similar
• Experience with Jira or GitLab APIs, webhooks, workflow automation, issue synchronization, merge request workflows, or repository automation
• Experience with relational or NoSQL databases, including schema design, query development, data modeling, or application integration
• Experience with secure software development practices, including authentication, authorization, role-based access control, API security, audit logging, and secure handling of sensitive data
• Experience with test automation, including unit testing, integration testing, API testing, end-to-end testing, or test-driven development
• Experience troubleshooting distributed applications or integrations, including API behavior, data mapping, authentication, system dependencies, observability, performance, and reliability
• Experience mentoring engineers, leading technical design discussions, performing code reviews, or establishing engineering best practices
• Strong written and verbal communication skills, including the ability to communicate technical concepts to technical and non-technical stakeholders