MLOPS Engineer
Skills
About the role
This is an MLOps engineering role at Cognizant in Chennai, working hybrid, aimed at candidates with 6 to 15 years of experience. The role focuses on raising MLOps maturity across the organization by building deployment, monitoring and retraining pipelines for data science projects. You'd work hands-on with Kubernetes, cloud ML platforms and CI/CD pipelines to operationalize machine learning models.
What you’ll do
- Research and implement MLOps tools, frameworks and platforms for data science projects
- Build deployment, inference, monitoring and retraining pipelines
- Set up drift detection for data drift and model drift
- Set up experiment tracking and MLOps architecture
- Publish models via REST APIs
- Conduct internal training and presentations on MLOps tools
- Raise MLOps maturity in the organization through a backlog of initiatives
What they’re looking for
- 6 to 15 years of experience
- Wide experience with Kubernetes
- Experience operationalizing data science projects (MLOps) with at least one framework such as Kubeflow, AWS SageMaker, Google AI Platform, Azure ML or DataRobot
- Good understanding of ML and AI concepts with hands-on model development experience
- Proficiency in Python for ML and automation, plus Bash and Unix command line
- Experience implementing CI/CD/CT pipelines
Nice to have
- Experience with cloud platforms, preferably AWS
What’s on offer
- Retirement plans and financial education benefits
- Paid time off, flexible working where possible, healthcare plans and counselling
- Access to a large internal talent marketplace across 350,000+ roles
Questions about this role
Is this role remote or in office?
It's hybrid, based in Chennai, India.
How many years of experience do I need?
6 to 15 years of experience.
What's the notice period?
The posting asks for immediate availability up to a 90-day notice period.
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