Key Responsibilities

Design, develop, and maintain end-to-end MLOps pipelines to automate the machine learning lifecycle from training to deployment and monitoring.
Collaborate with data scientists, ML engineers, and platform teams to operationalize ML models across cloud and hybrid environments.
Build and manage containerized environments for training and inference using Docker and Kubernetes.
Implement CI/CD workflows (e.g., GitHub Actions, Jenkins) for deploying ML models.
Ensure observability and monitoring of models in production (latency, drift, performance, errors).
Support model deployment to a variety of targets including APIs, applications, dashboards, and edge devices.
Implement model versioning, rollback strategies, governance, and traceability using tools like MLflow or Kubeflow.
Drive best practices across teams and provide technical mentorship on MLOps topics.
Continuously evaluate and integrate new tools and technologies to improve MLOps capabilities.

Required Skills & Qualifications

8+ years of experience in software, data, or ML engineering, with 6+ years in MLOps.
Strong programming experience in Python, SQL, and Spark/PySpark.
Deep expertise in MLOps tools such as MLflow, Kubeflow, Airflow, etc.
Experience with cloud platforms, preferably GCP (Vertex AI, GKE, Cloud Run) or AWS.
Hands-on with Databricks, FastAPI, Docker, Kubernetes.
Proficient with CI/CD, Git, and Infrastructure as Code (Terraform, Ansible).
Knowledge of monitoring frameworks like Prometheus and Grafana.
Experience in GCP , AWS is also acceptable.
Strong communication and stakeholder management skills.

Preferred Qualifications

Experience with building scalable, self-service ML infrastructure.
Familiarity with model governance, compliance, and security in production environments.
Prior work in building reusable and modular MLOps solutions for cross-functional teams.

Senior ML Ops Engineer