Government and Infrastructure - AI and Data - AI Automation Engineer - Manager

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• Governm ent and Infrastructure – Technology Consulting - AI & Data – AI Automation Engineer - Manager
• Your key responsibilities
• Implement retrieval-augmented generation (RAG) and hybrid (keyword and vector) search over portfolio evidence so analysts and client stakeholders can query the portfolio in natural language with source citations.
• Load extracted entities and relationships into graph and relational data stores to support dependency analysis, change-impact analysis, cluster detection and modernization wave sequencing.
• Build evaluation harnesses — golden datasets, grounding and faithfulness checks, accuracy measures and regression tests for prompts and agents — and track quality metrics over time.
• Design human-in-the-loop review points and capture reviewer decisions, model and prompt versions, and rationale to maintain end-to-end traceability of AI-supported findings.
• Instrument pipelines for observability, throughput and token/cost consumption, and optimize for performance and cost at portfolio scale.
• Package prompts, agents, tools and pipeline components as reusable, documented assets for subsequent modernization, enhancement and operations work.
• Produce technical documentation, runbooks and knowledge-transfer materials for the capabilities you build.
• Lead the AI & Automation team: plan and prioritize work, manage delivery against 90-day milestones and remove blockers.
• Own the architecture of the automated assessment platform, including model and tool selection, data flows, security boundary, scalability and cost.
• Establish responsible-AI governance for the program — model risk management, transparency, auditability and human-review policy — aligned with federal AI guidance and the NIST AI Risk Management Framework.
• Serve as the primary technical point of contact with the client on AI methods; present results and explain methodology to client leadership.
• Coordinate closely with the Application Modernization lead so automated findings and manual validation form a single, consistent evidence base.
• Recruit, mentor and develop engineers, and contribute to the staffing plan as the program scales.
• Skills and attributes for success
• Understanding of generative AI models (openAI, Anthropic, open-source LLMs) into web-based products
• Experience working with designers, product managers, AI/ML engineers to create intelligent user experience
• Experience writing tests, participating in code reviews
• Seeking out information to learn about emerging methodologies and technologies
• Clarifying problems by driving to understand the true issue
• Looking for opportunities for improving methods and outcomes

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