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Senior MLOps Engineer

Senior MLOps Engineer

Atomic NorthIndia, India, India
6 hours ago
Job description

🚀 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.
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