Tech Lead – ML Ops Engineer (ID:3419)

As a Tech Lead – ML Ops Engineer, you will: Lead the design, development, deployment, and operation of production-grade machine learning systems. Build and maintain end-to-end ML pipelines for model training, validation, deployment, monitoring, and lifecycle management. Drive ML Ops and ML Platform development, ensuring scalable, reliable, and production-ready ML workflows. Support ML use cases such as recommendations, forecasting, and automation. Work with tools such as Airflow, Azure ML, and FastAPI to deliver robust ML services. Automate model build and deployment workflows using CI/CD pipelines (GitHub Actions, Azure DevOps). Ensure high reliability, observability, and performance of ML platforms. Collaborate with Data Scientists, Engineers, and Product Managers to productionise ML research and models. Implement monitoring, alerting, and model-drift detection using tools like Azure Monitor, NewRelic, and custom logging frameworks. Design and manage ML infrastructure using Terraform, Docker, and container-based platforms. What You Bring to the Table: 6–8 years of experience in ML Engineering, ML Ops, Data Engineering, or DevOps roles with exposure to the full ML lifecycle. Strong proficiency in Python (primary), with working knowledge of SQL and Bash. Hands-on experience with ML frameworks and tools such as MLflow, Scikit-learn, and/or PyTorch. Proven experience building and maintaining ML pipelines and workflows. Solid experience with cloud platforms, particularly Azure and AWS. Strong understanding of containerisation and orchestration, including Docker and Kubernetes. Experience with CI/CD tools such as GitHub Actions and Azure DevOps. Hands-on exposure to infrastructure-as-code using Terraform. Familiarity with data platforms such as Snowflake, Delta Lake, Redis, and Azure Data Lake. You Should Possess the Ability to: Lead and guide technical implementation of ML Ops best practices. Translate ML research and prototypes into scalable, production-ready systems. Design and operate reliable ML pipelines with strong monitoring and observability. Automate deployment and operational workflows to improve efficiency and stability. Troubleshoot and optimise ML systems for performance and scalability. What We Bring to the Table: Opportunity to work on end-to-end ML platforms and large-scale production ML systems. Exposure to modern ML Ops tooling and cloud-native infrastructure. Hands-on experience with monitoring, automation, and scalable ML infrastructure. Continuous learning through real-world implementation of ML, DevOps, and cloud technologies. Let’s Connect Want to discuss this opportunity in more detail? Feel free to reach out. Recruiter: Asha Krishnan Phone: +31 20 369 0609 ; Extn :132 Email: asha.k@stafide.nl LinkedIn: https://www.linkedin.com/in/asha-krishnan