AI / ML Engineer
Accenturefederalservices · Tampa, FL
About this role
**AI / ML Engineer** At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer and life better for people. Join a collaborative, caring community where you’re empowered to grow through hands-on experience, certifications, and training. We’re 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. **Responsibilities** - Design, develop, and maintain **RAG pipelines** (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 with data engineers, architects, and mission SMEs **Qualifications** - Hands-on experience with **LLMs**, prompt engineering, embeddings, vector databases, and RAG frameworks - Strong **Python** skills; Java/C++ is a plus. Proficiency with ML/DL frameworks (**PyTorch, TensorFlow, HuggingFace**) - Solid understanding of algorithms, data structures, APIs, and distributed systems. Experience with **AWS or Azure** and **Docker**; ability to work across structured and unstructured datasets **Preferred Skills** - Experience building **production-ready AI/ML systems**, including **CI/CD** or **MLOps** frameworks (e.g., MLflow, BentoML) - Understanding of data governance, security constraints, and model risk (additional details not included in the provided text)
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