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.
The projected compensation range for this position is $98,000.00-$163,100.00. There are differentiating factors that can impact a final salary/hourly rate, including, but not limited to, Contract Wage Determination, relevant work experience, skills and competencies that align to the specified role, geographic location (For Remote Opportunities), education and certifications as well as Federal Government Contract Labor categories. In addition, MANTECH invests in its employees beyond just compensation. MANTECH’s benefits offerings include, dependent upon position, Health Insurance, Life Insurance, Paid Time Off, Holiday Pay, short-term and long-term Disability, Retirement and Savings, Learning and Development opportunities, wellness programs as well as other optional benefit elections.
MANTECH considers all qualified applicants for employment without regard to disability or veteran status or any other status protected under any federal, state, or local law or regulation.
If you need a reasonable accommodation to apply for a position with MANTECH, please email us at careers@mantech.com and provide your name and contact information.
