Senior AI Automation Engineer – Gov & Infra

EY

McLean (VA)

In loco

USD 120.000 - 180.000

Tempo pieno

10 giorni fa

Generatore di candidature

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Descrizione del lavoro

EY in McLean, VA is seeking an AI Automation Senior Engineer to design, build, and operate AI and automation capabilities for a government and infrastructure portfolio. You will convert raw data into structured intelligence, enabling rationalization and modernization decisions at scale.

You will work closely with Application Modernization Engineers and other stakeholders to improve accuracy, build reusable assets, and deliver end-to-end AI capabilities while aligning with federal and regulated

Competenze

Bachelor's degree in CS, 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.

Mansioni

Develop static-analysis and LLM-assisted extraction to identify components and dependencies.
Build LLM-based capabilities for code and business-logic summarization, tagging and detection to surface candidates.
Implement retrieval-augmented generation (RAG) and hybrid search over portfolio evidence with source citations.
Load extracted entities and relationships into graph and relational data stores for dependency analysis.
Create evaluation harnesses, grounding checks and regression tests for prompts and agents.
Design human-in-the-loop review points and maintain end-to-end traceability of AI findings.
Instrument pipelines for observability, throughput and cost at portfolio scale.
Package prompts, agents, tools and pipeline components as reusable assets.
Produce technical documentation and runbooks for capabilities.
Own end-to-end design and delivery of pipeline components or AI capabilities.
Design multi-step agent workflows and MCP tools with guardrails.
Tune models, embedding strategies, and retrieval approaches based on evaluation results.
Review code and coach Staff engineers on AI engineering practices.
Explain AI-derived findings to client stakeholders.