Lead Generative AI Engineer

CGI•Reston, VA

About The Position

We are seeking an experienced technical lead to design, build, and scale Generative AI and agentic solutions in a complex enterprise environment. This role combines hands-on engineering, solution architecture, and technical leadership, with a focus on moving AI solutions from proof of concept into secure, reliable production systems. The ideal candidate is platform-agnostic, has strong Python and cloud experience, and is comfortable leading teams while remaining actively involved in design and development. Experience with Google Cloud Platform and Vertex AI is preferred.

Requirements

6+ years of experience in software engineering, data engineering, machine learning engineering, or a related field.
Proven experience deploying and supporting AI or machine learning solutions in production.
Strong experience with Generative AI, large language models, RAG, prompt engineering, and context engineering.
Advanced proficiency in Python.
Experience with GCP, AWS, Azure, or a comparable cloud platform.
Experience with APIs, microservices, containerization, Docker, and Kubernetes.
Knowledge of CI/CD, ML Ops, LLM Ops, monitoring, and AI evaluation practices.
Demonstrated ability to lead technical teams and communicate with senior stakeholders.

Nice To Haves

Experience with Google Cloud Platform and Vertex AI.
Experience with Palantir Foundry.
Experience building AI agents using Lang Graph, Agent Development Kit, or custom frameworks.
Knowledge of traditional machine learning techniques, including classification, regression, and clustering.
Experience designing hybrid AI solutions that balance accuracy, performance, latency, and cost.

Responsibilities

Lead the architecture and development of Generative AI, Retrieval-Augmented Generation, and agentic AI solutions.
Build and scale AI applications from prototype through production deployment.
Design secure, scalable integrations across cloud, data, and enterprise platforms.
Develop RAG pipelines, multi-agent workflows, APIs, and microservices.
Establish engineering standards, conduct code reviews, and mentor technical teams.
Implement CI/CD, MLOps, automated testing, monitoring, and observability.
Evaluate AI solutions for quality, hallucinations, latency, reliability, and cost.
Partner with business, architecture, security, and data teams to translate requirements into technical solutions.
Implement responsible AI guardrails related to privacy, security, bias, and human oversight.
Create reusable AI components, frameworks, and development standards.