MLOPS Engineer

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

Job summary

Cognizant is seeking an experienced MLOps specialist to operationalize and scale machine learning initiatives. The role focuses on deploying, monitoring, and retraining ML models, with emphasis on automation and reproducibility across cloud environments.

You will advance MLOps maturity, build robust pipelines, and advocate best practices in data science workflows, collaborating with cross-functional teams in a fast-paced setting.

Qualifications

• Wide experience with Kubernetes.
• Operationalization of DS projects using Kubeflow, SageMaker, or similar.
• Strong Python for ML and automation.
• Experience in CI/CD/CT pipelines.
• Cloud platforms experience, AWS preferred.
• Good understanding of ML/AI concepts.

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.

Skills

Python

Bash

Unix

ML concepts

Model development

CI/CD pipelines

REST APIs

Tools

Kubernetes

Kubeflow

AWS SageMaker

Azure ML Studio

Google AI Platform

DataRobot

DKube

CI/CD pipelines

GitHub Actions

Terraform

FastAPI

Job description

Role – MLOPS
Location : Pan Indida
Experience : 5+ to 12 Years
Mode of Interview - In Person (Chennai)

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.