AI Deployment Lead

GRACE is ARPA-H’s AI assistant, and its value depends on how well ARPA-Humans put it to work. We are looking for a startup-minded AI Deployment Lead with hands-on experience designing agent and multi-agent workflows on top of existing LLM platforms, and a track record of training people to use new tools well. You will own the relationship between GRACE and the program teams across ARPA-H, from first training session through ongoing use, and you’ll be the primary channel bringing what you learn back to the GRACE product, engineering, and design team. This role is ideal for someone who has worked directly with customers or internal teams in high-velocity environments including startups or big tech, listens more than they talk, and wants to apply practical AI across an enterprise. You’ll do this while helping teams understand GRACE’s capabilities and limits within the constraints of our mission and the broader federal environment including privacy, security, safety, and responsible use by default. What You’ll Work On: Work directly with program teams to identify high-value workflows, and build working prompt structures, agent chains, and templates that solve real problems. Build a reusable library of prompt patterns, agent templates, and playbooks other teams can pick up directly. Run onboarding sessions, office hours, and team-specific training tailored to how different groups across ARPA-H work. Grow a network of power users and champions across program teams. Sit in on real workflows, gather what’s working and what’s broken, and turn it into clear, prioritized input for the GRACE team. Translate ambiguous requests into specific product requirements. Track usage, training completion, and workflow adoption across the agency. Build a clear view of where adoption is working and where it needs attention and bring it to leadership regularly. Establish and improve training materials, playbooks, and best practices as GRACE’s capabilities evolve., Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS) As a Capital One Machine Learning Engineer (MLE), you’ll be part of an Agi…

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5+ years of experience in a customer-facing technical role such as product management or product operations, technical account management, or solutions engineering Experience with solutions engineering, technical account management, sales engineering, or forward deployed work Experience in high-velocity environments including tech startups or large-scale industry roles where you owned a customer or stakeholder relationship end to end Experience building with modern LLM tools, including prompt engineering and agent or workflow construction Experience with product management or product operations, with an instinct for prioritizing feedback and translating it into requirements Experience driving adoption of a new tool or process across a busy organization Ability to train non-technical audiences on technical tools Ability to navigate ambiguity and figure out the next right step without a lot of direction Ability to prioritize feedback and translate it into requirements Bachelor’s degree Nice If You Have: Experience with agent frameworks or workflow builders including LangChain, CrewAI, or low-code tools such as Zapier or Make Experience with Model Context Protocol (MCP) Possession of excellent active listening skills to collaborate Possession of excellent customer service and people reading skills to anticipate their needs

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Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $86,800.00 to $198,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the Posting Date. Identity Statement As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud. Candidate AI Usage Policy AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided. Work Model Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings. Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility. Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility. Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role. Commitment to Non-Discrimination