ModelOps Engineer

MANTECH seeks a motivated, career and customer-oriented ModelOps Engineer to join our team in Ashburn, VA. This is a hybrid-remote position requiring two days onsite in Ashburn, VA and three days remote.

In this role, you will collaborate within a cross-functional team to integrate new Artificial Intelligence / Machine Learning (AI/ML) solutions into operational pipelines to deliver mission impact for U.S. Customs and Border Protection (CBP). The ideal candidate brings deep expertise in predictive modeling lifecycles, hands-on experience with machine learning frameworks, and a pragmatic, customer-centric approach to applying ML models to solve complex operational challenges.

Each day, CBP oversees the massive flow of people, capital, and products that enter and depart the United States via air, land, sea, and cyberspace. The volume and complexity of both physical and virtual border crossings require the application of solutions to aid officers in detecting threats while promoting efficient trade and travel.  

Responsibilities include but are not limited to:

• Lead the integration and deployment of trained AI/ML models into production environments (e.g., cloud, edge devices) using Machine Learning Operations (MLOps) best practices.
• Develop and optimize model training and inference pipelines for real-time execution while efficiently handling large-scale data processing.
• Work with data science teams to structure automated ML model health monitoring, performance tracking, and model refresh capabilities.
• Implement Continuous Integration, Continuous Delivery, and Continuous Training (CI/CD/CT) workflows using commercial and open-source modeling platforms and services.
• Coordinate with Data Science and Data Engineering teams to build scalable feature stores for optimal model training and execution workflows.
• Research, evaluate, and recommend new MLOps engineering tools, applications, and software packages that can be approved and adopted for use in the CBP environment.
• Collaborate with cross-functional teams (e.g., Software Engineering, Data Science) to integrate and test candidate AI/ML models and applications for operational assessment.

Minimum Qualifications:

• Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Information Technology, Artificial Intelligence, or a related technical field with 7+ years of experience (or equivalent combination of education and experience, ranging from High School Diploma + 15 years to M.S. + 5 years or PhD + 3 years).
• Experience with MLOps tools, orchestration frameworks (e.g., MLflow, Kubeflow, Apache Airflow, or similar) and/or automated drift detection or monitoring platforms (e.g., Alibi, Grafana, Prometheus).
• Experience with enterprise ML platforms (such as AWS SageMaker, Databricks, DataRobot, or similar cloud-native AI services).
• Experience automating workflow orchestration to manage both batch and real-time streaming data processing for model inference.
• Proficiency in Python, Scala, or Java, along with a strong understanding of high-performance computing, parallel processing, and Graphics Processing Unit (GPU) acceleration.
• Knowledge of productionizing ML models—including optimizing for inference latency and containerization (e.g., Docker, Kubernetes)—or exposure to multi-cloud deployment platforms (such as AWS, Azure, GCP, or comparable environments).

Preferred Qualifications:

• Experience working with distributed data/computing tools and search/indexing platforms (e.g., Apache Spark, Elasticsearch, Solr, Hadoop, Impala, PostgreSQL, or related relational/non-relational data systems).
• Deep understanding of MLOps principles and tools for automated model training, testing, deployment, governance, and continuous monitoring.
• Strong communication skills with a proven ability to collaborate effectively across Data Science, Data Engineering, and DevSecOps teams.
• Experience with data integration and Extract-Transform-Load / Extract-Load-Transform (ETL/ELT) workflows across relational/non-relational databases (such as Oracle, PostgreSQL, MongoDB) and cloud serverless endpoints (e.g., AWS Lambda, GraphQL).
• Experience leveraging deep learning frameworks (such as PyTorch, TensorFlow, or Keras) and computer vision libraries (e.g., OpenCV, SimpleITK, VTK).
• Experience with biometric or image recognition algorithms and associated predictive analytics pipelines.
• Experience managing GPU-based infrastructure and execution performance optimization.

Clearance Requirements:

• Must possess an active Top Secret security clearance OR current DHS CBP Suitability (applicants without active DHS CBP Suitability must hold an active Top Secret or a higher-level clearance in order to be considered).
• Must be able to obtain and maintain full DHS CBP Suitability prior to start.

Physical Requirements:

• The person in this position needs to occasionally move about inside the office to access file cabinets, office machinery, or to communicate with co-workers, management, and customers, which may involve delivering presentations.