Senior AI/ML & Data 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. Our 13,000+ people are united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. Join Accenture Federal Services, a technology company within global Accenture. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and caring community where you feel like you belong and are empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more. Join us to drive positive, lasting change that moves missions and the government forward! Job Description : We are looking for an experienced Senior AI/ML and Data Engineer to develop, implement, and maintain sophisticated machine learning, LLM, and enterprise AI solutions for our federal client. The ideal candidate combines strong hands on engineering talent with architectural leadership—capable of shaping mission aligned AI strategy, designing scalable pipelines, and delivering production-grade ML and Generative AI capabilities in secure environments. This role will partner with cross functional teams - including 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 machine learning models, LLM applications, retrieval augmented generation (RAG) pipelines, and agentic AI systems. Build data preprocessing, training, fine tuning, inference, and evaluation workflows. Develop scalable ML pipelines using modern toolchains (SageMaker, Bedrock, Azure ML, Databricks, Ray, HuggingFace). Implement MLOps solutions including CI/CD for ML, model versioning, monitoring, logging, and drift detection. Shape AI system design decisions including vector DB selection, embedding strategies, prompt architecture, and model selection. Define target state architectures for LLM enabled applications, AI microservices, RAG pipelines, and knowledge retrieval systems. Data & Cloud Engineering : Design, build, and maintain scalable automated data pipelines (ETL/ELT) to support both batch and real-time data processing. Architect data lakes and warehouses (e.g., Snowflake, Databricks, BigQuery) to ensure high availability and performance for ML workflows. Implement rigorous data quality checks and validation frameworks to ensure "garbage-in, garbage-out" never applies to our models. Delivery & Stakeholder Engagement : Work closely 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 of experience in one or more of the following areas: 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 (OpenAI, Anthropic, Bedrock, Azure OpenAI, HuggingFace Transformers). Experience architecting ML pipelines using AWS, Azure, or GCP. Familiarity with DevSecOps and IaC tools (Terraform, CloudFormation, Jenkins, GitLab, etc.). Experience implementing microservices, APIs, and containerized workloads (Docker, Kubernetes, ECS/EKS/AKS). Bonus points if you have : Experience building RAG pipelines with vector databases (Pinecone, FAISS, Weaviate, Milvus). Experience designing agentic workflows and multi agent AI systems. Experience with graph databases, knowledge graphs, or semantic search. Certifications such…