Senior MLOps Engineer (AWS Certified)

Job Title: Senior MLOps Engineer (AWS Certified)

Experience Required: 6+ Years

Location: Bangalore -Onsite/Hybrid

About the Role

We are looking for a highly experienced Senior MLOps Engineer to lead the design, deployment, and optimization of machine learning systems at scale. This role requires deep expertise in AWS cloud services, MLOps best practices, and production-grade ML system management. The ideal candidate will bridge the gap between data science and engineering by building robust, automated, and scalable ML pipelines.

Key Responsibilities

• Architect and implement scalable, secure, and reliable MLOps frameworks on AWS.
• Design and manage end-to-end ML lifecycle pipelines, including data ingestion, training, validation, deployment, and monitoring.
• Deploy machine learning models into production using AWS Sage Maker and containerized environments.
• Build and maintain CI/CD pipelines for ML workflows using tools such as AWS Code Pipeline, Jenkins, or GitHub Actions.
• Automate infrastructure provisioning using Infrastructure as Code (IaC) tools (Terraform, AWS CloudFormation).
• Implement monitoring, logging, and alerting solutions using AWS CloudWatch and third-party tools.
• Ensure model performance, scalability, and reliability through continuous evaluation and optimization.
• Collaborate with data scientists, software engineers, and product teams to deliver end-to-end ML solutions.
• Enforce best practices in security, compliance, and cost optimization within AWS environments.

Required Qualifications

• Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.
• 6+ years of experience in MLOps, DevOps, Machine Learning Engineering, or related roles.
• Strong hands-on experience with AWS services, including Sage Maker, S3, EC2, Lambda, EKS/ECS, and CloudWatch.
• Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience with Docker and Kubernetes for containerization and orchestration.
• Solid understanding of CI/CD principles, version control (Git), and automated deployment strategies.
• Experience with workflow orchestration tools (Airflow, Step Functions, or similar).
• Strong knowledge of data pipelines and data engineering concepts.

Preferred Qualifications

• Active AWS Certification(s):
• AWS Certified Machine Learning Specialty
• AWS Certified DevOps Engineer Professional
• AWS Certified Solutions Architect Associate/Professional
• Experience with MLOps platforms such as MLflow, Kubeflow, or SageMaker Pipelines.
• Familiarity with big data technologies like Apache Spark or Hadoop.
• Experience in monitoring ML models for drift, bias, and performance degradation.
• Exposure to Agile/Scrum methodologies.