About the Role:
We are looking for a skilled MLOps Engineer to build, deploy, automate, and maintain scalable Machine Learning and AI solutions. The role involves managing ML pipelines, model deployment, monitoring, and cloud infrastructure.
Experience:
2–5 years of experience in MLOps, Machine Learning, DevOps, or Cloud Engineering.
Hands-on experience with ML model deployment and production environments.
Key Responsibilities:
Build and manage end-to-end ML pipelines.
Deploy, monitor, and maintain ML models in production.
Implement CI/CD for machine learning workflows.
Automate model training, testing, deployment, and monitoring.
Manage cloud-based ML infrastructure and resources.
Implement model versioning, data versioning, and experiment tracking.
Collaborate with Data Scientists, ML Engineers, and DevOps teams.
Required Skills:
Strong knowledge of Python, Machine Learning, and DevOps.
Experience with Docker, Kubernetes, Git, and CI/CD tools.
Knowledge of MLflow, Kubeflow, Airflow, or similar MLOps tools.
Hands-on experience with AWS / Azure / GCP.
Understanding of model monitoring, logging, and cloud infrastructure.
Good problem-solving and communication skills.
Qualification:
Bachelor’s/Master’s degree in Computer Science, IT, AI/ML, Data Science, or a related field.
