Description

Build and operate machine learning systems from training through production.

This role is on-site.

Responsibilities

• Design and ship end-to-end ML CI/CD pipelines covering training, validation, and production rollout with safe rollbacks.
• Build feature stores and model registries to ensure reproducibility, traceability, and fast experimentation.
• Automate data pipelines for structured and unstructured data to power reliable model training.
• Scale distributed training on GPU clusters, optimizing performance and resource usage.
• Run production ML/AI workloads on Kubernetes, implementing autoscaling and GPU orchestration.

Required Skills

• 5+ years of experience in ML engineering.
• Expertise in Python.
• Hands-on experience with MLflow for experiment tracking and model management.
• Proficiency with containerization using Docker and orchestration with Kubernetes.
• Experience managing infrastructure as code using Terraform.
• Deep knowledge of CI/CD practices.
• Familiarity with data workflow orchestration tools like Apache Airflow.
• Experience implementing observability using Prometheus and Grafana.
• Working knowledge of Apache Kafka for data streaming.

Key Skills

Education

Any Graduate