MLOPS Engineer

Cognizant; Bengaluru; On-site; INR 10,00,000 - 20,00,000; Full time

Job summary

A leading technology firm seeks an experienced MLOps Engineer to drive the operationalization of Data Science projects. Key responsibilities include researching MLOps tools, enhancing MLOps maturity, and conducting training sessions. The ideal candidate has extensive experience with Kubernetes and MLOps frameworks, as well as strong Python skills. Familiarity with cloud platforms, especially AWS, is preferred.

Qualifications

• Wide experience with Kubernetes.
• Experience in operationalization of Data Science projects (MLOps) using popular frameworks.
• Good understanding of ML and AI concepts.
• Proficiency in Python used for ML and automation tasks.
• Experience in CI/CD/CT pipelines implementation.
• Experience with cloud platforms, preferably AWS.

Responsibilities

• Research and implement MLOps tools and frameworks.
• Work on backlog activities to raise MLOps maturity.
• Introduce a modern and automated approach to Data Science.
• Conduct training and presentations on MLOps tools.

Skills

AWS SageMaker

Azure ML Studio

GCP Vertex AI

PySpark

Azure Databricks

MLFlow

KubeFlow

AirFlow

Github Actions

AWS CodePipeline

Kubernetes

AKS

Terraform

Fast API

Job description

Role : MLOps Engineer
Location - 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.