AWS Data & AI Engineer, Senior

Booz Allen Hamilton•Usa, DC

13h

About The Position

AWS Data & AI Engineer, Senior The Opportunity: AI solutions only succeed when they are built on reliable data platforms and production-ready ML workflows. As an AWS Data and AI Engineer, you’ll enable intelligent, scalable systems by engineering the data pipelines and machine learning foundations that move models from experimentation to mission-ready deployment. In this role, you’ll focus on designing, building, and operationalizing machine learning solutions on AWS, with a strong emphasis on Amazon SageMaker. You’ll work alongside solution architects, data scientists, and application teams to deliver secure, scalable ML pipelines—supporting everything from data ingestion and feature engineering to model training, deployment, and monitoring in compliance-driven federal environments. This role is ideal for an engineer who enjoys hands-on development, building ML platforms, and growing into a senior AI or ML engineering role within AWS-centric ecosystems. Work with us and help build the future of AI-enabled systems in the Federal Government. Join us. The world can’t wait.

Requirements

4+ years of experience as a data engineer, ML engineer, or software engineer working with data-driven or ML-enabled systems
Experience designing and operating end-to-end ML workflows using Amazon SageMaker, including SageMaker Studio or Notebooks, training jobs and hyperparameter tuning, managed model endpoints and batch inference, SageMaker Pipelines, Model Registry, and experiment tracking
Experience building data pipelines and feature engineering workflows using AWS services, such as S3, Glue, Redshift, EMR, Athena, or Lambda
Experience with Python development for data processing and ML workloads and SQL
Experience deploying and managing containerized ML workloads using Docker, ECR, and AWS-managed compute
Knowledge of ML frameworks and libraries commonly used with SageMaker, such as PyTorch, TensorFlow, scikit-learn, or XGBoost
Knowledge of MLOps concepts, including CI/CD for ML, model versioning, monitoring, and retraining
Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
Bachelor’s degree in Computer Science, Engineering, or Data Science
Ability to obtain an AWS Certification, such as AWS Machine Learning – Specialty or AWS Solutions Architect – Associate, within 3 months of start date

Nice To Haves

Experience implementing production MLOps pipelines using SageMaker Pipelines, Step Functions, or CI/CD tools
Experience supporting FedRAMP or ATO-driven cloud environments
Experience operationalizing models developed by data scientists or research teams
Experience working with OpenAI models or APIs, including integrating large language models into applications, building prompt-based workflows, or supporting GenAI use cases
Experience working in Agile or DevSecOps teams
Knowledge of GenAI or foundation model workflows using SageMaker, such as JumpStart, managed foundation models, or custom LLM deployments
Knowledge of IAM, VPC networking, encryption, and security controls for ML workloads in regulated environments