> Markdown version of [/jobs/ext/3570917-government-and-infrastructure-ai-and-data-ai-automation-engineer-manager](https://www.wearedevelopers.com/jobs/ext/3570917-government-and-infrastructure-ai-and-data-ai-automation-engineer-manager). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Government and Infrastructure - AI and Data - AI Automation Engineer - Manager - **Company:** Ernst & Young LLP - **Location:** McLean, VA, United States - **Experience:** Expert - **Salary:** $150,700.0 - $251,200.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Java (Programming Language), JavaScript (Programming Language), .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Component-Based Software Engineering, Application Portfolio Management, Microsoft Azure, Cluster Analysis, Adobe Coldfusion, Static Program Analysis, Code Review, Information Systems, Databases, Computer Engineering, Relational Databases, Graph Database, PostgreSQL, Machine Learning, Neo4j, Open Source Technology, Parsing, Regression Testing, OpenAI, Search Technologies, Software Engineering, Data Streaming, Datadog, Data Ingestion, LangChain, Large Language Models, Technical Debt, Generative AI, Agentic-AI, SC Clearance, Build Management, Information Technology, Cosmos DB, Machine Learning Operations, Azure AI, Evaluation of Large Language Models, Model Context Protocol, Semantic Kernel, Container Apps, Key Vault, Service Stack - **Published:** October 3, 2026 - **Apply:** https://careers.ey.com/talentcommunity/apply/1444060233/?locale=en_US ## About the Role · 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 · Solving problems by incorporating data into decision making To qualify for the role, you must have · 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 · 6-10+ 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 Ideally, you'll also have · 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. Due to the nature of our work in Government and Infrastructure, work may be required to be completed at client, EY and/or contractor sites. Our goal is to assign professionals to projects within a commutable distance of their work location office. In certain circumstances, travel may be required beyond your work location based on client and project needs. Candidates should be willing to travel 20 - 30% or more. ## Description · 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. · 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.