Tech Lead – ML Ops Engineer

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 (Git Hub Actions, Azure Dev Ops).
• Ensure high reliability, observability, and performance of ML platforms.
• Collaborate with Data Scientists, Engineers, and Product Managers to product ionise ML research and models.
• Implement monitoring, alerting, and model-drift detection using tools like Azure Monitor, New Relic, and custom logging frameworks.
• Design and manage ML infrastructure using Terraform, Docker, and container-based platforms.

• 6–8 years of experience in ML Engineering, ML Ops, Data Engineering, or Dev Ops 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 Git Hub Actions and Azure Dev Ops.
• Hands-on exposure to infrastructure-as-code using Terraform.
• Familiarity with data platforms such as Snowflake, Delta Lake, Redis, and Azure Data Lake.

• Lead and guide technical implementation of ML Ops best practices.
• Translate ML research and prototypes Олим 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.

• 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, Dev Ops, and cloud technologies.

Want to discuss this opportunity in more detail? Feel free to reach out.

Recruiter
:
Asha Krishnan
Phone
: +31 20 369 VectO ;
Extn :132
Email
: asha.k
Linked In: