MLOPS Engineer

Cognizant; Gurugram District; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Cognizant is seeking an experienced MLOPS Engineer to join a Pan India footprint. You will deploy and monitor ML models, automate pipelines, and drive MLOps maturity across DS projects.

The role emphasizes Kubernetes-based deployment, cloud ML platforms, and end-to-end ML lifecycle management. Responsibilities include training teams, implementing agile processes, and enabling scalable inference pipelines with monitoring and retraining capabilities.

Qualifications

• Hands-on experience in deploying and operationalizing ML models
• Strong understanding of ML/AI concepts and model lifecycle
• Proficiency in Python for ML and automation tasks
• Experience building and maintaining CI/CD/CT pipelines for ML workloads
• Experience with Kubernetes-based deployments and cloud platforms (AWS preferred)
• Familiarity with data and model drift monitoring and experiment tracking

Responsibilities

• Research and implement MLOps tools and platforms for Data Science projects
• Drive MLOps maturity through a backlog of activities
• Promote modern, automated Data Science approaches in the organization
• Conduct internal training and knowledge sharing on MLOps tools

Skills

Kubernetes

Python

CI/CD

ML/AI concepts

Bash/Unix

REST APIs

Tools

Kubeflow

AWS SageMaker

Azure ML Studio

GCP Vertex AI

AirFlow

GitHub Actions

AWS CodePipeline

Terraform

FastAPI

AKS

Job description

Role: MLOPS Engineer
Location: Pan India
Experience: 6 to 15 Years
Notice Period : Immediate to 90 days
Mode of Interview : In-Person

Key words -Skillset

• AWS SageMaker, Azure ML Studio, GCP Vertex AI
• PySpark, Azure Databricks
• MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline
• Kubernetes, AKS, Terraform, Fast API

Responsibilities

• Model Deployment, Model Monitoring, Model Retraining
• Deployment pipeline, Inference pipeline, Monitoring pipeline, Retraining pipeline
• Drift Detection, Data Drift, Model Drift
• Experiment Tracking
• MLOps Architecture
• REST API publishing

Job Responsibilities:

• Research and implement MLOps tools, frameworks and platforms for our Data Science projects.
• Work on a backlog of activities to raise MLOps maturity in the organization.
• Proactively introduce a modern, agile and automated approach to Data Science.
• Conduct internal training and presentations about MLOps tools’ benefits and usage.

Required experience and qualifications:

• Wide experience with Kubernetes.
• Experience in operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube).
• Good understanding of ML and AI concepts. Hands-on experience in ML model development.
• Proficiency in Python used both for ML and automation tasks. Good knowledge of Bash and Unix command line toolkit.
• Experience in CI/CD/CT pipelines implementation.
• Experience with cloud platforms - preferably AWS - would be an advantage.