Senior MLOps Engineer

Hiring : MLOps Engineer

Experience : 5–8 Years (with at least 2+ years in MLOps or ML deployment roles)

Location : Bengaluru, Bhopal, Gurgaon, Hyderabad, Jaipur, Mumbai, Pune , Chennai

About the Role

We are seeking a skilled MLOps Engineer to operationalize and scale machine learning solutions on AWS. The ideal candidate will have strong expertise in cloud infrastructure, automation, and ML lifecycle management — from model training to deployment and monitoring — ensuring efficient and secure ML operations.

Key Responsibilities

Design and implement end-to-end MLOps pipelines using AWS SageMaker, Lambda, and Step Functions.

Automate model deployment, monitoring, and retraining workflows for high availability and performance.

Collaborate with data scientists to streamline model experimentation, versioning, and governance.

Manage infrastructure-as-code using Terraform or CloudFormation for scalable and repeatable environments.

Implement CI / CD pipelines for ML models and data workflows using Jenkins, GitLab, or similar tools.

Set up and maintain monitoring, logging, and alerting systems with AWS CloudWatch and native services.

Enforce model governance, approval workflows, and compliance practices.

Partner with DevOps and Data Engineering teams to standardize MLOps best practices.

Continuously optimize ML model performance, reliability, and cost efficiency.

Skills & Requirements

5–8 years of experience, with 2+ years in MLOps or ML deployment roles.

Strong knowledge of AWS services : SageMaker, Lambda, S3, Glue, CloudWatch.

Proficient in Python and infrastructure automation using Terraform or CloudFormation .

Experience with CI / CD tools , model monitoring , and orchestration (AWS Step Functions, Airflow).

Understanding of data governance , model approval workflows , and audit practices .

Good to Have

AWS Certifications (Machine Learning – Specialty, Solutions Architect).

Exposure to AWS Redshift , Athena , or Feature Stores .

Familiarity with Docker , Kubernetes , or EKS for containerized ML deployments.

Experience with MLflow or SageMaker Model Registry for model management.

Understanding of feature engineering pipelines and data versioning tools (DVC, Git LFS).

Education

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.