Tech Lead – ML Ops Engineer

Amsterdam - Netherlands

Monthly Salary: Not Disclosed

Experience Required: 6-8years

Posted on: 10 hours ago

Vacancies: 1 Vacancy

Job Summary

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:

68 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.

Lets Connect

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

Recruiter: Asha Krishnan

Phone:; Extn :132

Email:

LinkedIn: Skills:

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: 68 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. Lets Connect Want to discuss this opportunity in more detail Feel free to reach out. Recruiter: Asha Krishnan Phone:; Extn :132 Email: LinkedIn: