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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # SME Machine Learning Ops Engineer - **Company:** Rapid Fire Protection, Inc. - **Location:** Ashburn, VA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Adobe Analytics, Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Computer Vision, Microsoft Azure, Big Data, Biometrics, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Apache Hadoop, Intrusion Detection and Prevention, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, OpenCV, Open Source Technology, Oracle (Applications), Performance Tuning, Tensorflow, Azure Machine Learning, Scala (Programming Language), Smart Devices, Software Engineering, Data Streaming, Data Processing, Google Cloud, Enterprise Software Applications, High Performance Computing, Pytorch, Grafana, Apache Spark, Deep Learning, Multi-Cloud, Keras, Containerization, Kubernetes, Apache Kafka, Non-relational Database, Graphql, Data Management, Machine Learning Operations, Devsecops, Docker, Databricks - **Published:** June 15, 2026 - **Apply:** https://www.dice.com/job-detail/c4836825-6646-416b-9fdb-0898995d9b15 ## About the Role Adtech seeks a motivated, career and customer-oriented SME Machine Learning Ops Engineer. This is currently a hybrid position with two days onsite in Ashburn, VA and three days remote. In this role, you will collaborate within a cross-functional team to develop new Artificial Intelligence/Machine Learning (AI/ML) based solutions into operational pipelines to deliver mission impact for U.S. Customs and Border Protection (CBP). The ideal candidate will have deep expertise and experience with predictive modeling lifecycles, hands-on experience with machine learning tools and frameworks, and a pragmatic, customer-centric approach to applying ML models to solve complex problems., * HS Diploma/GED and 20+ years of experience, AS/AA and 18+ years, BS/BA and 12+ years, MS/MA/MBA and 9+ years, or PhD/Doctorate and 7+ years * Expertise with MLOps tools and frameworks such as Mlflow, Kubeflow, Airflow and implementing monitoring/drift detection capabilities (e.g. Alibi, Grafana) * Experience with ML platforms, such as AWS Sagemaker, DataBricks or DataRobot * Experience automating workflow orchestration to handle both batch and real-time streaming data processing for model inference * Hands-on experience productionizing models, including experience optimizing for inference speed, containerization (e.g., Docker), and with multi-cloud deployment platforms (e.g., AWS, Azure, Google Cloud Platform) * Proficiency in Python, Scala and Java with strong understanding of high-performance computing and GPU acceleration * Hands-on experience with Big Data tools (e.g. Spark, Hadoop, Kafka), * Experience with MLOps principles and tools for automated model training, testing, deployment and monitoring * Strong communication skills with the ability to collaborate effectively across Data Science, Data Engineering, and DevSecOps teams * Experience with data engineering Extract, Transform and Load (ETL) workflows across various relational/non-relational databases (Oracle/Postgres, MongoDB) and cloud endpoint services e.g. (Lambda, GraphQL etc.) * Experience in using deep learning frameworks (PyTorch, TensorFlow, Keras) and computer vision libraries (OpenCV, SimpleITK, ITKm VTK) * Experience with biometric or image recognition algorithms and associated predictive analytics pipelines * Experience with GPU-based infrastructure and performance optimization ## Description * Lead the integration and deployment of trained AI/ML models into production environments (e.g., cloud, edge devices) using MLOps best practices * Develop and optimize model training & inference pipelines for real-time execution, and efficiently handle large-scale data processing * Work with data science teams to structure automated ML model health monitoring and refresh capabilities * Implement continuous integration, delivery and training (CI/CD/CT) workflows with commercial and open-source modeling platforms/services * Coordinate with Data Science and Engineering teams to build scalable feature stores for optimal model training & execution workflows * Research, evaluate and recommend new tools, applications, software packages for MLOps engineering that can be adopted and approved for use in the CBP environment * Collaborate with cross-functional teams (e.g., Software Engineering, Data Science) to integrate and test multiple 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) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [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) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)