AI / ML Engineer

Accenture Federal Services•Tampa, FL

About The Position

At Accenture Federal Services, our purpose is to help the US federal government make the nation stronger and safer, and improve people's lives. Our 13,000+ employees are dedicated to leveraging technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. We are seeking an AI Engineer with significant experience in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to design, develop, and optimize intelligent systems that address complex mission and enterprise challenges. This role combines modern Generative AI engineering with traditional computer science and machine learning principles, 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.
An active TS/SCI is required.

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.