Forward Deployed AI Engineer responsible for embedding with internal teams to build AI agents, workflow automation, MCP integrations, reusable patterns, and production-ready AI capabilities. Job Requirements Bachelor's degree with 8+ years of relevant experience, Master's degree with 5+ years of relevant experience, or PhD with 3+ years of relevant experience Strong hands-on software engineering experience Advanced Python development experience Experience shipping production software to end users Applied experience building with large language models Experience with LLM orchestration, retrieval, tool calling, prompt design, and evaluation Experience building MCP servers, API integrations, or equivalent production connectors Systems integration experience across APIs, data pipelines, event streams, and authentication Experience building AI agents and workflow automation Experience supporting production deployments and ownership transition Experience developing reusable technical patterns and implementation frameworks Preferred Skills Experience delivering technical solutions within the Department of Defense, intelligence community, or defense industrial base Experience working in accredited, restricted, or controlled environments Prior consulting, solutions engineering, forward deployed engineering, or internal enablement experience Experience with self-hosted or open-weight model deployments Experience with workflow automation platforms Familiarity with low-code tooling used by business teams Experience in a functional business domain such as finance, supply chain, contracts, or operations Experience developing data connectors and enterprise system integrations Job Responsibilities Embed with internal teams to identify and implement AI-enabled workflows Build and deploy AI agents and workflow automation solutions Develop MCP servers, APIs, data connectors, and system integrations Integrate AI capabilities with enterprise applications, data pipelines, event streams, and authentication systems Develop reusable patterns, components, and implementation standards for AI solutions Support deployment of AI applications into production environments Transfer ownership of completed solutions to internal teams Develop technical documentation and implementation guidance Support training and enablement for internal builders Assist teams with adopting approved AI development patterns and tooling Support self-hosted or open-weight model deployments when required Build automation solutions using both custom software and applicable workflow platforms

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