AI / ML Engineer

Accenture Federal Services•Arlington, VA

18h

About The Position

At Accenture Federal Services, our purpose is to help the US federal government make the nation stronger and safer, and improve the lives of its citizens. Our team of over 13,000 professionals is dedicated to leveraging technology and innovation for clients across defense, national security, public safety, civilian, and military health organizations. As a technology company within the global Accenture network, we offer a supportive and collaborative environment recognized by Glassdoor as a Top 100 Best Place to Work. Here, you'll be empowered to grow and thrive through hands-on experience, certifications, and industry training, driving positive and lasting change for government missions.

Requirements

Hands-on experience building and deploying AI/ML applications using cloud-based AI services.
Strong understanding of production-ready pipelines for cloud or on‑prem environments.
Practical experience implementing GenAI models in real-world solutions.
Familiarity with foundational AI/ML techniques such as deep learning, neural networks, chatbots, or image processing.

Nice To Haves

Experience designing or contributing to standardized model deployment strategies (tooling, CI/CD, testing, validation).
Prior work integrating AI/ML monitoring into Observability platforms.
Exposure to enterprise-scale AI/ML operations and collaboration with cross‑functional platform teams (like DEA or MLOps units).

Responsibilities

Develop applications and systems that leverage AI tools, Cloud AI services, and both cloud-based and on‑premise pipelines with production‑ready quality.
Apply GenAI models as part of end‑to‑end solution development.
Implement solutions that may involve deep learning, neural networks, chatbots, and image processing.
Collaborate with the DEA team to standardize the model deployment strategy, including tooling, CI/CD, and testing/validation processes.
Integrate robust AI monitoring into existing project Observability capabilities to ensure reliable and transparent model performance.