Lead AI Engineer

BigBear.ai; Columbia (MD); On-site; USD 180,000 - 240,000; Full time

Job summary

BigBear.ai is seeking a Lead AI Engineer to drive the design, development, and deployment of generative AI and agentic systems for DoW operations. You will lead AI architecture, mentor engineers, and collaborate with government stakeholders to deliver production-ready AI capabilities.

The role blends hands-on engineering with technical leadership, spanning full-stack development, cloud data pipelines, and security/ATO/FedRAMP compliance, with emphasis on performance and reliability in

Job description

Residency

All applicants must currently reside in the United States

Overview

BigBear.ai is looking for a Lead AI Engineer to drive the design, development, and deployment of generative AI and agentic systems for mission-critical Department of War (DoW) operations. This role combines hands‑on engineering with technical leadership. You will set AI architecture direction, lead and mentor engineers, and work directly with government stakeholders to turn operational needs into production‑ready AI capabilities.

BigBear.ai is one of the fastest‑growing AI/ML companies in the industry, focused on technology‑driven decision support for the DoW. Our team combines subject matter expertise, user community engagement, sensitivity to operations, and world‑class software engineering. Together we help government customers gain insight into complex problems, run scenarios quickly, and make sense of large and disparate datasets across the DoW.

What you will do

As Lead AI Engineer, you will own the technical direction of AI‑enabled solutions from discovery through deployment. You will work with data scientists, systems analysts, product managers, and mission users to deliver AI systems that meet the highest standards of performance, reliability, and security.

Key Responsibilities

• AI Architecture & Technical Leadership: Define and lead the architecture for generative AI solutions, including multi‑agent workflows, retrieval‑augmented generation (RAG), and LLM integration. Set engineering standards and best practices for building AI systems.


• Agentic Systems & MCP Development: Design, build, and deploy multi‑agent orchestration and custom Model Context Protocol (MCP) servers that connect AI agents securely to existing enterprise and mission systems.


• LLM Integration & Optimization: Build applications against commercial and government‑approved LLM endpoints (e.g., Anthropic, OpenAI, Ask Sage). Apply advanced prompt engineering, dataset routing, and confidence gating to optimize output quality and reliability.


• Customer Discovery & Prototyping: Engage directly with government stakeholders to gather requirements, assess feasibility within time and cost constraints, and rapidly prototype solutions that prove mission value before scaling.


• Full‑Stack Development: Design, develop, document, test, and debug applications using full‑stack principles across front‑end, back‑end, API, and data layers.


• Cloud & Data Pipelines: Shape and implement cloud‑forward data environments and web/data pipelines that feed AI workflows, including document ingestion, classification, and knowledge‑domain routing.


• AI Evaluation, Security & Assurance: Build evaluation harnesses for regression testing, citation verification, and retrieval transparency. Red‑team AI systems for prompt injection and adversarial failure modes before production. Ensure solutions meet DoW security and compliance requirements (ATO, FedRAMP).


• Team Leadership & Mentorship: Lead and mentor engineers on AI tooling, AI‑first development practices, and adoption strategies. Promote collaboration through initiatives such as hackathons and knowledge sharing.


• Roadmap Influence: Feed field‑discovered gaps and user feedback into product roadmaps, and work with product leadership on release planning and prioritization.


• Deployment & Monitoring: Coordinate system installations, monitor production performance, and ensure deployed AI capabilities meet specifications and mission expectations.

What you need to have

• Education: Bachelor's degree in computer science, Software Engineering, or a related field with 15 years of experience


• Demonstrated experience leading engineering teams or serving as technical lead on complex software initiatives


• Hands‑on experience designing and deploying generative AI or agentic AI systems in operational or production environments


• Experience with multi‑agent orchestration, RAG, prompt engineering (few‑shot, chain‑of‑thought), and LLM API integration


• Proficiency in front‑end and back‑end development


• Experience with key technologies:




• Python, .NET / C#, Angular, TypeScript, Docker, AWS services, SQL Server, Web/Data Pipelines, Git‑based CI/CD


• Equivalent technologies are acceptable, such as React (instead of Angular), Java (instead of .NET), PostgreSQL (instead of SQL Server), or Azure (instead of AWS)




• Experience delivering software in secure, regulated environments (ATO, FedRAMP, security hardening)


• Problem‑solving skills: Strong analytical abilities with a focus on delivering high‑quality, mission‑ready solutions


• Communication: Ability to explain AI capabilities, limitations, and trade‑offs clearly to both technical and non‑technical stakeholders

What we'd like you to have

• Experience building custom Model Context Protocol (MCP) servers


• Experience with AI‑assisted development tools (e.g., Claude Code, Cursor, Copilot Agents)


• Experience with Helm/YAML, GitLab, Qlik, or Databricks


• Experience building AI evaluation frameworks or doing adversarial testing of LLM systems


• Background in data warehousing, ETL, or business intelligence


• Experience integrating AI into enterprise workflow tools (e.g., Power Automate custom connectors)

Pay transparency

Please note the targeted compensation range is provided as an estimate, and any actual compensation offer may vary depending on the needs of the company, or an applicant's skillset, competencies, experience, education, certifications, location, or other factors. The estimated range does not include the value of any benefits offered.

About BigBear.ai

BigBear.ai is a leading provider of AI‑powered decision intelligence solutions for national security, supply chain management, and digital identity. Customers and partners rely on Bigbear.ai's predictive analytics capabilities in highly complex, distributed, mission‑based operating environments. Headquartered in McLean, Virginia, BigBear.ai is a public company traded on the NYSE under the symbol BBAI. For more information, visit https://bigbear.ai/ and follow BigBear.ai on LinkedIn: @BigBear.ai and X: @BigBearai.

BigBear.ai is an Equal opportunity employer all protected groups, including protected veterans and individuals with disabilities.