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

EY•McLean, VA

1d•$106,900 - $200,600•Hybrid

About The Position

At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. Government and Infrastructure – Technology Consulting - AI & Data – AI Automation Engineer - Senior Consultant From strategy to execution, Government and Infrastructure practice of Ernst & Young provides a full range of consulting and audit services to help our Federal, State, Local and Education clients implement new ideas to help achieve their mission outcomes. We deliver real change and measurable results through our diverse, high-performing teams, quality work at the highest professional standards, operational know-how from across our global organization, and creative and bold ideas that drive innovation. We enable our government clients to achieve their mission of protecting the nation and serving the people; increasing public safety; improving healthcare for our military, veterans, and citizens; delivering essential public services; and helping those in need. EY is ready to help our government build a better working world. EY delivers unparalleled service in big data, business intelligence, and digital analytics built on a blend of custom-developed methods related to customer analytics, data visualization, and optimization. We leverage best practices and a high degree of business acumen that has been compiled over years of experience to ensure the highest level of execution and satisfaction for our clients. At EY, our methods are not tied to any specific platforms but rather arrived at by analyzing business needs and making sure that the solutions delivered meet all client goals. The opportunity You will help our clients navigate the complex world of modern AI, data and analytics. We’ll look to you to provide our clients with a unique business perspective on how data science and analytics can transform and improve their entire organization – starting with key business issues they face. This is a high growth, high visibility area with plenty of opportunities to enhance your skillset and build your career. The AI & Automation Senior Engineer designs, builds and operates the AI and automation capabilities that allow the team to assess a large legacy application portfolio at a speed and scale that manual analysis cannot achieve. This role converts raw source code, database schemas, configuration and technical documentation into structured, evidence-backed portfolio intelligence — component inventories, dependency graphs, capability maps, technical-debt and risk indicators, and consolidation candidates — that directly drive rationalization and modernization decisions. AI & Automation Engineers work side by side with Application Modernization Engineers, who validate automated findings and feed confirmed patterns back into the pipeline, continuously improving accuracy. The capabilities built here become reusable assets for later modernization, enhancement and operations work across the program.

Requirements

Must have a Bachelor's degree in computer science, software engineering, information systems, computer engineering or a related field, or equivalent practical experience.
Must be eligible to obtain and maintain a Secret Clearance
3-6+ years of software, data or ML engineering experience, including architecting and delivering production AI/ML systems, leading teams of five or more engineers, and client-facing delivery in federal or regulated environments
Must be comfortable working in-person as needed in the Washington, DC area

Nice To Haves

Static code analysis and parsing (Tree-sitter, Roslyn, JavaParser or abstract syntax tree tooling).
Graph databases (Azure Cosmos DB Gremlin API, Neo4j) and graph algorithms such as clustering and community detection.
Agent frameworks (LangGraph, LangChain, Semantic Kernel) and Model Context Protocol (MCP).
LLM evaluation and observability tooling.
Azure services including Container Apps, AI Search, Cosmos DB, PostgreSQL, Service Bus, Key Vault, Entra ID and private networking; Azure Government experience.
Experience with legacy application modernization or application portfolio assessment.
Microsoft Certified: Azure AI Engineer Associate (AI-102) or comparable certification.
Prior federal government delivery experience.

Responsibilities

Build and operate automated ingestion pipelines for source code repositories, database scripts and schemas, build and deployment configuration, and technical documentation across heterogeneous technology stacks (.NET, Java, ColdFusion, JavaScript, batch and legacy runtimes).
Develop static-analysis and LLM-assisted extraction to identify application components, technology fingerprints (languages, frameworks, versions and libraries), dependencies, interfaces, data-access patterns and shared database usage.
Build LLM-based capabilities for code and business-logic summarization, business-capability tagging, functional similarity and duplicate detection to surface consolidation candidates, and technical-debt, security and risk indicators.
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.