AI/ML Engineer

Accenture Federal Services•Tampa, FL

2h•$108,400 - $185,100•Hybrid

About The Position

At Accenture Federal Services, our purpose is to help the US federal government make the nation stronger and safer, and improve lives. We are a technology company within global Accenture, with over 13,000 employees dedicated to leveraging technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and supportive community where you can grow, learn, and thrive through hands-on experience, certifications, and industry training. Join us to drive positive, lasting change that advances missions and the government forward. We are seeking an AI Engineer with strong experience in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to design, build, and optimize intelligent systems that solve complex mission and enterprise challenges. This role combines modern GenAI engineering with traditional computer science and machine learning, supporting both rapid prototyping and production-grade delivery.

Requirements

Hands-on experience with LLMs, prompt engineering, embeddings, vector databases, and RAG frameworks.
Strong programming skills in Python; familiarity with Java/C++ is a plus.
Proficiency with ML and DL frameworks (PyTorch, TensorFlow, HuggingFace).
Solid understanding of algorithms, data structures, APIs, and distributed systems.
Experience with cloud platforms (AWS or Azure) and containerization (Docker).
Ability to work across structured and unstructured datasets.
Active TS/SCI Clearance

Nice To Haves

Experience building production-ready AI/ML systems, including CI/CD or MLOps frameworks (MLFlow/BentoML).
Understanding of data governance, security constraints, and model risk management.
Ability to communicate complex technical concepts to non-technical stakeholders.

Responsibilities

Design, develop, and maintain RAG pipelines, including document ingestion, embedding generation, vector storage, retrieval logic, and LLM orchestration.
Build and optimize LLM-powered applications for classification, summarization, Q&A, knowledge retrieval, and workflow automation.
Apply core software engineering and ML fundamentals to ensure performance, reliability, and security (e.g., data structures, algorithms, model evaluation, MLOps, API development).
Implement and tune traditional ML models when required (e.g., regression, clustering, feature engineering, classical NLP).
Integrate cloud-native services (Azure/AWS), data pipelines, and containerized workloads (Docker).
Collaborate closely with cross-functional teams—including data engineers, architects, and mission SMEs—to translate requirements into scalable solutions.