Senior AI/ML & Data Engineer
Accenturefederalservices · Chantilly, VA
About this role
**Senior AI/ML & Data Engineer** **About Accenture Federal Services** At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer—improving life for people. Join a collaborative, caring community focused on growth, learning, and hands-on experience. **Job Description** We’re looking for an experienced **Senior AI/ML & Data Engineer** to develop, implement, and maintain **machine learning, LLM, and enterprise AI** solutions for a federal client. You’ll combine hands-on engineering with architectural leadership to shape mission-aligned AI strategy, design scalable pipelines, and deliver production-grade ML and Generative AI capabilities in **secure environments**. You’ll partner with cross-functional teams (data engineering, cloud engineering, cybersecurity, and mission SMEs) to architect end-to-end AI systems that are reliable, compliant, and impactful. **The work you’ll do** - **AI/ML Engineering** - Design, develop, and deploy ML models, LLM applications, **RAG pipelines**, and **agentic AI** systems - Build data preprocessing, training, fine-tuning, inference, and evaluation workflows - Develop scalable ML pipelines using modern toolchains (e.g., **SageMaker, Bedrock, Azure ML, Databricks, Ray, HuggingFace**) - Implement **MLOps** (CI/CD for ML, model versioning, monitoring, logging, drift detection) - Shape AI system design decisions (vector DB selection, embedding strategies, prompt architecture, model selection) - Define target-state architectures for LLM-enabled apps, AI microservices, RAG pipelines, and knowledge retrieval systems - **Data & Cloud Engineering** - Design, build, and maintain scalable automated data pipelines (**ETL/ELT**) for batch and real-time processing - Architect data lakes/warehouses (e.g., **Snowflake, Databricks, BigQuery**) for ML workflow performance and availability - Implement rigorous data quality checks and validation frameworks - **Delivery & Stakeholder Engagement** - Partner with program leadership, technical SMEs, and mission stakeholders to define requirements and AI roadmaps - Translate business problems into technical AI solutions and communicate tradeoffs to mixed audiences - Produce architecture diagrams, interface specifications, deployment patterns, and integration plans **Here’s what you’ll need** - Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or related field - **5+ years** in one or more of: - AI/ML engineering, cloud-native development, or data engineering - Strong proficiency in **Python** and ML frameworks (**PyTorch, TensorFlow, Scikit-learn**) - Hands-on experience with **LLM development** (e.g., OpenAI, Anthropic, Bedrock, Azure OpenAI, HuggingFace Transformers) - Experience architecting ML pipelines using **AWS, Azure, or GCP** - Familiarity with **DevSecOps** and **IaC** tools (e.g., Terraform, CloudFormation, Jenkins, GitLab) - Experience implementing **microservices, APIs, and containerized workloads** (Docker, Kubernetes, ECS/EKS/AKS) **Bonus points if you have** - RAG pipelines with vector databases (Pinecone, FAISS, Weaviate, Milvus) - Agentic workflows and multi-agent AI systems - Graph databases, knowledge graphs, or semantic search - Certifications (e.g., AWS Architect, AWS ML Specialty, Azure AI Engineer, Security+) - Ability to translate complex technical concepts for non-technical audiences - Strong problem-solving with a product-foc
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