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

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• Governm ent and Infrastructure – Technology Consulting  - AI & Data – AI Automation Engineer  - Senior Consultant
• 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.
• Own the design and delivery of one or more pipeline components or AI capabilities end to end, from requirements through production operation.
• Design multi-step agent workflows and tool integrations, including Model Context Protocol (MCP) tools, with appropriate guardrails and failure handling.
• Select and tune models, embedding strategies, chunking and retrieval approaches based on measured evaluation results.
• Review the code, prompts and evaluation results of Staff engineers and coach them on AI engineering practices.
• Explain AI-derived findings, methods and limitations clearly to client technical stakeholders and application owners.
• 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
• Collaborating, influencing, and building consensus through constructive relationships and effective listening

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