The workAs an AI Engineer in this role, you will drive the operationalization of advanced AI and agentic AI systems within production mission environments. Your work will center on implementing a modern Hub-and-Spoke architecture designed to accelerate enterprise AI adoption, support mission-critical applications, and enable robust AI governance.
You will be responsible for integrating AI solutions with DevSecOps, data engineering, platform engineering, and cybersecurity practices, ensuring 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 tailored to complex, real-world missions.
Key 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
Here's what you need:- 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
Nice to have:- 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
Eligibility requirements:- US Citizen
- An active TS/SCI federal security clearance is required
As required by local law, Accenture Federal Services provides reasonable ranges of compensation for hired roles based on labor costs in the states of
California, Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, New Jersey, New York, Washington, Vermont, the District of Columbia, and the city of Cleveland. The base pay range for this position in these locations is shown below. Compensation for roles at Accenture Federal Services varies depending on a wide array of factors, including but not limited to office location, role, skill set, and level of experience. Accenture Federal Services offers a wide variety of benefits. You can find more information on benefits here. We accept applications on an on-going basis and there is no fixed deadline to apply.
The pay range for the states of California, Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, New Jersey, New York, Washington, Vermont, the District of Columbia, and the city of Cleveland is:
$116,900-$243,100 USD
