Mlops engineer

Location –  Remote / fulltime

Experience – 6 to 10 years

Budget – 1L to 1.50L

JD:-

Job Summary:

We are looking for a highly skilled and experienced Senior MLOps Engineer to join our team. As an MLOps Engineer, you will play a key role in deploying, scaling, and monitoring machine learning models in production environments. You will work closely with data scientists, ML engineers, and DevOps teams to bridge the gap between ML model development and operations.

Key Responsibilities:

• Design, implement, and maintain scalable MLOps pipelines using industry best practices.
• Automate the deployment and monitoring of ML models across environments (dev/stage/prod).
• Develop CI/CD pipelines for machine learning workflows.
• Implement model versioning, model registry, and tracking tools (e.g., MLflow, DVC, Kubeflow).
• Monitor model performance in production and handle model retraining and rollback strategies.
• Collaborate with cross-functional teams to support ML model deployment and production integration.
• Ensure compliance, security, and governance in the ML lifecycle.
• Optimize compute resources and costs for ML workloads using cloud-native services.

Required Skills and Experience:

• 6–10 years of experience in MLOps, DevOps, or related roles.
• Strong experience with cloud platforms (AWS/GCP/Azure) for deploying ML models.
• Proficient in containerization and orchestration (Docker, Kubernetes).
• Experience with CI/CD tools (Jenkins, GitLab CI/CD, Argo Workflows).
• Familiarity with ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Expertise in ML lifecycle tools like MLflow, Kubeflow, DVC, Airflow.
• Solid scripting and automation skills using Python, Bash, or Shell.
• Hands-on experience with data versioning, feature stores, and monitoring tools.
• Knowledge of security best practices, performance tuning, and cost optimization.

Nice to Have:

• Experience with Terraform or Infrastructure as Code (IaC).
• Understanding of data engineering workflows and streaming pipelines.
• Experience in edge ML deployment, A/B testing, or Canary deployments.

Perks & Benefits:

• 100% Remote Work Flexibility
• Opportunity to work on cutting-edge ML/AI projects
• Collaborative team environment
• Competitive compensation