What's the role?
In this role, you will partner closely with ML engineers, researchers, data teams, and platform teams to design scalable infrastructure, automate deployment workflows, and establish engineering standards that ensure reliability, observability, and reproducibility across the machine learning lifecycle.

• Design and implement end-to-end machine learning pipelines covering data ingestion, training, evaluation, deployment, monitoring, and retraining.

• Develop scalable infrastructure that enables consistent and repeatable movement of models from research to production.

• Own model-serving architectures for both batch and real-time inference workloads.

• Establish CI/CD practices for machine learning, including automated testing, model packaging, version control, and deployment automation.

• Build and maintain containerized and orchestrated settings using technologies such as Docker and Kubernetes.

• Optimize infrastructure utilization for compute and GPU-intensive workloads while balancing performance and cost efficiency.





• Implement model and data versioning, reproducibility standards, and rollback mechanisms.

• Develop monitoring, alerting, and observability frameworks for production ML systems.

• Implement mechanisms for detecting data drift, model degradation, latency issues, and operational risks.

• Support continuous feedback loops, human-in-the-loop workflows, and retraining processes that improve model quality over time.

• Translate complex operational challenges into scalable, secure, and maintainable platform solutions.

• Evaluate emerging MLOps technologies, orchestration frameworks, and industry best practices to guide tooling decisions.

Who are you?
You bring a robust combination of machine learning infrastructure expertise, software engineering excellence, and a passion for building reliable production systems.

• You have a Bachelor or Master's degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning

📌 Sr Ml Mumbai
🏢 HERE Technologies
📍 Mumbai