AI ENGINEER

Role overview

Responsibilities

• check_circle Agentic Solution Design & Development: Lead the design and development of AI-driven solutions from conception to deployment, building multi-agent workflows in which coordinated agents and prompts work together to produce outputs supporting reporting and dissemination. This includes prototyping, writing production-quality code, and maintaining deployed AI capabilities.
• check_circle LLM Integration: Integrate existing foundation models into the RAD architecture via APIs and orchestration frameworks, selecting the right model and approach for each task rather than building models from scratch.
• check_circle Prompt Engineering & Workflow Automation: Design, test, and refine prompts and agent instructions to reliably automate manual tasks, and decompose complex mission workflows into discrete steps an agent pipeline can execute.
• check_circle Evaluation & Reliability: Develop robust testing, evaluation, and validation strategies for AI-generated outputs, ensuring accuracy, consistency, and appropriate handling of edge cases before outputs reach analysts and reports.
• check_circle Collaboration & Communication: Serve as a key technical liaison, collaborating with cross-functional teams including system engineers, software developers, and domain experts. Effectively present and articulate recommended AI approaches, discussing the tradeoffs and implications of different implementations with both technical and non-technical stakeholders.
• check_circle Continuous Improvement: Stay current with the rapidly evolving LLM and agentic AI landscape (new models, orchestration frameworks, and tooling), continuously identifying practical opportunities to apply emerging capabilities to the system.

Basic qualifications

• Education: Bachelor of Arts/Bachelor of Science
• Experience: 8+ years of related experience
• Security clearance level: TS/ SCI W/ Polygraph
• **US citizenship required

Preferred qualifications

• Experience with retrieval-augmented generation (RAG) pipelines and vector databases
• Familiarity with LLM observability and evaluation tooling
• Experience conducting exploratory data analysis on structured and unstructured datasets to prepare inputs for AI workflows
• Familiarity with MLOps/LLMOps practices for deploying and monitoring AI capabilities at scale
• Familiarity with domain knowledge surrounding government agency reporting and dissemination policies

Benefits

• check_circle Growth: AI-powered career tool that identifies career steps and learning opportunities
• check_circle Support: An internal mobility team focused on helping you achieve your career goals
• check_circle Rewards: Comprehensive benefits and wellness packages, 401K with company match, and competitive pay and paid time off
• check_circle Community: Award-winning culture of innovation and a military-friendly workplace

About the company