AI Engineer

Accenture Federal Services•Arlington, VA

About The Position

At Accenture Federal Services, our purpose is to make the US federal government stronger and safer, and to improve people's lives. We are a technology company within global Accenture, with over 13,000 employees dedicated to serving clients across defense, national security, public safety, civilian, and military health organizations. We foster a collaborative and supportive community where employees can grow and thrive through hands-on experience, certifications, and industry training. Join us to drive meaningful change and advance government missions through technology and ingenuity. As an AI Engineer, you will be instrumental in operationalizing advanced AI and agentic AI systems within production mission environments. Your work will focus on implementing a modern Hub-and-Spoke architecture to accelerate enterprise AI adoption, support mission-critical applications, and ensure robust AI governance. You will integrate AI solutions with DevSecOps, data engineering, platform engineering, and cybersecurity practices to ensure the seamless delivery and continuous monitoring of scalable AI-powered software. Your contributions will directly impact the deployment of agentic AI systems, orchestration frameworks, and operational workflows designed for complex, real-world missions.

Requirements

Experience AI/ML production engineering, LLMOps or MLOps, and the deployment of Retrieval-Augmented Generation (RAG) architectures.
Familiarity with Kubernetes-based AI deployments, OpenAI-compatible APIs, and Python development
Experience in GPU inference optimization and working within NVIDIA GPU ecosystems
US Citizen
An active TS/SCI federal security clearance is required

Nice To Haves

Exposure to tools such as LangGraph, Semantic Kernel, vLLM, Ollama, Ray, and vector databases
Experience in integrating observability, cybersecurity, and scalable software delivery into AI platforms will help you succeed in supporting the continuous evolution and governance of enterprise AI systems

Responsibilities

Design, develop, and operationalize AI and agentic AI systems for production mission environments
Implement and optimize AI/ML production engineering workflows, including LLMOps and MLOps best practices
Architect and deploy Retrieval-Augmented Generation (RAG) solutions and AI orchestration frameworks
Manage Kubernetes-based AI deployments and ensure seamless integration with OpenAI-compatible APIs
Develop and maintain Python-based AI applications, focusing on GPU inference optimization
Leverage tools and frameworks such as LangGraph, Semantic Kernel, vLLM, Ollama, and Ray for scalable AI solutions
Integrate with NVIDIA GPU ecosystems and vector databases to enhance AI performance and scalability
Collaborate with cross-functional teams in AI, DevSecOps, data engineering, platform engineering, and cybersecurity
Support enterprise AI governance, continuous monitoring, and observability for mission-critical applications
Contribute to the delivery of scalable, secure, and reliable AI software within a modern Hub-and-Spoke architecture
Participate in the integration of operational workflows to accelerate AI adoption and mission impact