Time since we first saw it
Day 1
Early. Similar jobs usually come down between day 6 and day 69.
From 4,284 similar postings that came down. Coming down is not always a hire.
MLOPS Engineer
Coimbatore, Tamil Nadu, India
Actively hiring, first seen by us 1 day ago- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid remote
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingResearch and implement MLOps tools, frameworks and platforms for Data Science projects
Work through a backlog of activities to raise MLOps maturity
Introduce automated, agile approaches to Data Science delivery
Run internal training and presentations on MLOps tools and usage
From the employer’s posting
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.
What you’ll bring
All qualificationsCore experience
- Wide experience with Kubernetes
- Hands-on experience operationalizing ML projects with Kubeflow, SageMaker, Azure ML or similar
- Hands-on ML model development and good understanding of ML and AI concepts
- Proficiency in Python for ML and automation, plus Bash and Unix command line
- Experience implementing CI/CD/CT pipelines
Qualification wording
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.
Posting record
Report an errorActively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 3, 20261 day ago
- Recorded sightings
- 2
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Cognizant is hiring
140 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Takes jobs down quickly
- 4% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Bash
- Python
- AWS
- Azure
- Databricks
- GCP
- Kubernetes
- MLflow
- SageMaker
- Terraform
- Airflow
- PySpark
Where the posting names them
· 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.
Key words -Skillset - AWS SageMaker, Azure ML Studio, GCP Vertex AI - PySpark, Azure Databricks
- PySpark, Azure Databricks - MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline - Kubernetes, AKS, Terraform, Fast API
· 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.
· Experience in CI/CD/CT pipelines implementation. · Experience with cloud platforms - preferably AWS - would be an advantage.
- AWS SageMaker, Azure ML Studio, GCP Vertex AI - PySpark, Azure Databricks - MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline
- MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline - Kubernetes, AKS, Terraform, Fast API Responsibilities
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).
Your next step
Complete your application on careers.cognizant.com. The employer’s form will show what is required.
Already applied? Track this application
Has Cognizant posted this job before?
Yes. We’ve followed 6 other MLOPS Engineer jobs there since May 2026. All 6 are still up.
Large employers often post one title for several teams, so these can be separate openings.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Coimbatore, Tamil Nadu, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.