MLOps Engineer – Cognizant | PAN India | Immediate to 60 Days Joiners | Full-Time
Job Title: MLOps Engineer
Location: PAN India
Notice Period: Immediate to 60 Days
Employment Type: Full-Time
Work Model: Hybrid or Remote (as applicable)
Job Summary
Cognizant is hiring an experienced MLOps Engineer to streamline and scale machine learning operations across cloud environments. This role is ideal for professionals with a strong background in ML pipelines, Kubernetes, cloud platforms (AWS, Azure, GCP), and MLOps tools such as MLFlow, Kubeflow, and Airflow. You will play a critical role in building and operationalizing ML systems and pipelines for enterprise-scale AI initiatives.
Key Responsibilities
Design, implement, and maintain model deployment, monitoring, and retraining pipelines.
Build inference pipelines, drift detection systems (model and data), and experiment tracking setups.
Develop and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions and AWS CodePipeline.
Deploy models using FastAPI and REST APIs into Kubernetes environments such as EKS and AKS.
Collaborate with data scientists to automate model training using MLFlow or Kubeflow.
Define and manage MLOps architecture for scalable and reproducible deployments.
Deliver internal training and documentation on MLOps tooling and practices.
Partner with DevOps and Data Engineering teams for infrastructure provisioning using Terraform.
Continuously explore and integrate new tools to improve MLOps maturity.
Required Skills and Qualifications
Extensive experience with Kubernetes (AKS/EKS/GKE).
Proficiency with AWS SageMaker, Azure ML Studio, or GCP Vertex AI for model lifecycle management.
Strong understanding of ML lifecycle, including retraining, drift detection, and monitoring.
Solid experience in Python, Bash scripting, and Unix-based environments.
Familiarity with PySpark and Azure Databricks.
Hands-on with MLFlow, Kubeflow, Apache Airflow.
Experience with CI/CD tools such as GitHub Actions and AWS CodePipeline.
Knowledge of Terraform for infrastructure as code.
Clear understanding of REST API publishing and FastAPI-based deployment.
Experience working in production ML environments and delivering operationalized models.a
