AI/ML Engineer

Booz Allen Hamilton•Washington, DC

3d•$99,000 - $225,000

About The Position

As a Machine Learning Engineer on the Physical AI team, you will contribute to national defense research and development by designing, developing, and evolving tools and capabilities for a customer’s mission. You will support a prototyping team as it evaluates and integrates new technologies, translating real-world customer needs into technical specifications and customer-focused engineering solutions. The role involves developing and maintaining machine learning models and Azure-based data and ML pipelines, supporting cloud-native and containerized deployments, and helping evaluate emerging AI technologies. You will work with data science and engineering teams and communicate solutions, risks, and recommendations to government clients and other technical and non-technical stakeholders. Applicants must be able to obtain a Secret clearance. The projected annualized compensation range is $99,000 to $225,000. Booz Allen describes benefits including health, life, disability, financial and retirement benefits, paid leave, professional development, tuition assistance, work-life programs, dependent care, and recognition awards. The position’s work model and location are not specified in the posting.

Requirements

At least 5 years of experience developing AI, ML, or advanced analytics solutions.
Experience with Python and ML libraries, and building and deploying production ML systems.
Experience with agentic AI frameworks, LLM integrations, Azure OpenAI, or RAG architectures.
Ability to develop and maintain ML models for anomaly detection, data quality assessment, and predictive modeling, and evaluate model performance and effectiveness.
Ability to design and maintain Azure-based data and ML pipelines and scalable cloud-native solutions.
Ability to support Docker and Kubernetes deployments, monitor production workloads, troubleshoot operational issues, and maintain CI/CD, Infrastructure-as-Code, and configuration management practices.
Ability to explain technical concepts to non-technical stakeholders and communicate and collaborate effectively with government clients.
Ability to work collaboratively and independently and demonstrate initiative in completing complex tasks.
Ability to obtain a Secret clearance.
Bachelor’s degree in STEM.

Nice To Haves

Experience with Azure ML, Azure Functions, Azure Storage, Azure Data Factory, or Azure DevOps.
Experience combining ML approaches with deterministic business rules and scoring frameworks.
Experience supporting federal or highly regulated environments.
Strong written and verbal communication skills.

Responsibilities

Develop, enhance, and maintain ML models for anomaly detection, data quality assessment, and predictive modeling across workforce and operational datasets.
Evaluate model accuracy, performance, drift, and operational effectiveness; refine feature engineering and scoring strategies with data science and engineering teams.
Design, build, and maintain Azure-based data and ML pipelines, including automated scoring and model execution workflows.
Develop scalable cloud-native solutions for batch and event-driven processing, orchestration, scheduling, monitoring, and AI workload health management.
Support containerized deployments using Docker and Kubernetes; monitor production workloads, troubleshoot operational issues, and optimize reliability, performance, and cost efficiency.
Maintain CI/CD pipelines, Infrastructure-as-Code, and configuration management practices.
Support the prototyping team in evaluating and integrating new technologies into the customer’s mission.
Evaluate emerging AI technologies and contribute to agentic AI and orchestration proof-of-concepts.
Communicate technical concepts, solutions, risks, and recommendations to technical and non-technical stakeholders, including government clients.
Collaborate with data science and engineering teams and work independently to complete complex tasks.