Artificial Intelligence and Machine Learning Engineer, Lead

Booz Allen Hamilton•Usa, NC

4h•Remote

About The Position

Are you excited at the prospect of unlocking the secrets held by a data set? Are you fascinated by the possibilities presented by the IoT, machine learning, and artificial intelligence advances? As an MLOps Engineer, you have a passion for leveraging Department of War (DoW) data to drive operational readiness and tactical decision making. You excel at designing, building, and operationalizing scalable solutions in secure enterprise environments integrating datasets from disparate systems. As a member on our team, you’ll work directly with the client and stakeholders to architect data-driven solutions that enhance situational awareness and optimize mission execution. You will apply your deep technical knowledge in data orchestration and the development and deployment of solutions to production environments to inform the client’s technical strategy for designing and implementing complex systems. Join us. The world can't wait.

Requirements

Experience leading the deployment and operationalization of scalable AI microservices, including the development of production-grade APIs using containerization and orchestration, to deliver automated model serving and CI/CD pipelines for production application
Experience with data orchestration and containerization tools, such as Docker, Kubernetes, NIFI, or Airflow, to design, deploy and maintain scalable ETL/ELT pipelines, data flows, and production microservices to cloud environments
Experience leading or participating in cross-functional efforts to complete ATO accreditation for release of products in production environment
Experience implementing MLOps best practices, including model monitoring, drift detection, and automated retraining workflows, while maintaining strict compliance in regulated cloud environments
Experience in multiple programming and scripting languages, such as Python, Java, Bash, Shell, SQL, HCL, YAML, JavaScript, or Typescript to support full-stack development efforts
Experience in communicating complex technical concepts to both technical and non-technical audiences
Experience creating clear and comprehensive architecture diagrams, data flow diagrams, and system design documentation to support cross-functional collaboration and stakeholder alignment
Knowledge of NIST SP 800-53 controls
Secret clearance
Bachelor's degree in a Data Science or Mathematical field

Nice To Haves

Experience building ETL/ELT pipelines from external data lakes and systems such as the War Data Platform (WDP), Palantir Maven Smart System (MSS), Cloud Platforms such as Azure, AWS, or GCP, and SharePoint
Experience with other DoW enterprise data platforms such as Advana or MSS
Experience with Lean-Agile methodologies and frameworks such as Scrum or Scaled Agile Framework (SAFe)
Experience managing project workflows and documentation across Jira and Confluence for alignment to concurrent initiatives
Knowledge of USMC operational planning, intelligence cycles, or logistics processes
TS/SCI clearance
Master's degree in an Artificial Intelligence or Machine Learning related field

Responsibilities

Designing, building, and operationalizing scalable solutions in secure enterprise environments integrating datasets from disparate systems.
Working directly with the client and stakeholders to architect data-driven solutions that enhance situational awareness and optimize mission execution.
Applying deep technical knowledge in data orchestration and the development and deployment of solutions to production environments to inform the client’s technical strategy for designing and implementing complex systems.
Leading the deployment and operationalization of scalable AI microservices, including the development of production-grade APIs using containerization and orchestration, to deliver automated model serving and CI/CD pipelines for production applications.
Designing, deploying, and maintaining scalable ETL/ELT pipelines, data flows, and production microservices to cloud environments using data orchestration and containerization tools.
Leading or participating in cross-functional efforts to complete ATO accreditation for release of products in production environments.
Implementing MLOps best practices, including model monitoring, drift detection, and automated retraining workflows, while maintaining strict compliance in regulated cloud environments.
Communicating complex technical concepts to both technical and non-technical audiences.
Creating clear and comprehensive architecture diagrams, data flow diagrams, and system design documentation to support cross-functional collaboration and stakeholder alignment.