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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ModelOps Engineer - **Company:** Mantech International Corporation - **Location:** Ashburn, VA, United States (Remote available) - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Computer Vision, Microsoft Azure, Big Data, Biometrics, Cloud Computing, Cloud Engineering, Cloudera Impala, Continuous Delivery, Continuous Integration, Information Engineering, Data Integration, Extract Transform Load (ETL), Relational Databases, Distributed Data Store, Elasticsearch, Apache Hadoop, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, OpenCV, Open Source Technology, Oracle (Applications), Performance Tuning, Software Tools, Tensorflow, Prometheus, Azure Machine Learning, Software Engineering, Apache Solr, Data Streaming, Data Processing, Google Cloud, Enterprise Software Applications, High Performance Computing, Pytorch, Grafana, Apache Spark, Deep Learning, Multi-Cloud, Parallel Computation, AWS Lambda, Indexer, Keras, Containerization, AI Platforms, Kubernetes, Information Technology, Non-relational Database, Graphql, Machine Learning Operations, Devsecops, Serverless Computing, Docker, Databricks - **Published:** September 24, 2026 - **Apply:** https://dejobs.org/x/x/43B4B41EDAF640F596D3FF29CDFE1F28/job/ ## About the Role * 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. ## Description * 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. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Deepfakes in Realtime - How Neural Networks Are Changing Our World](https://www.wearedevelopers.com/videos/180-deepfakes-in-realtime-how-neural-networks-are-changing-our-world) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [The state of MLOps - machine learning in production at enterprise scale](https://www.wearedevelopers.com/videos/369-the-state-of-mlops-machine-learning-in-production-at-enterprise-scale) - [Unboxing the DeepFace](https://www.wearedevelopers.com/videos/335-unboxing-the-deepface) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)