About Our Client
Our client is a renowned name in the insurance space.
Job Description
12+ years in software engineering, DevOps, or ML Engineering with a focus on cloud-based ML pipelines
Strong experience with Amazon Web Services (AWS), especially :
Amazon SageMaker (training, deployment, Pipelines, Model Monitor)
S3, Lambda, Step Functions, CodePipeline, ECR, CloudWatch
Proficiency in Python, Bash, and scripting for automation
Familiarity with CI / CD tools like Jenkins, GitHub Actions, CodeBuild, etc.
Experience with Docker and container orchestration in AWS (, ECS, EKS optional)
Understanding of ML lifecycle, including feature engineering, training, deployment, and monitoring
Experience with data versioning and model tracking tools (, MLflow, DVC, SageMaker Model Registry)
Excellent communication and collaboration skills
The Successful Applicant
Key Responsibilities
Design & Implement MLOps Pipelines
Build and maintain robust CI / CD pipelines for ML using Amazon SageMaker Pipelines, CodePipeline, Step Functions, etc.
Automate model training, evaluation, deployment, and monitoring processes.
Infrastructure & Cloud Management
Use Infrastructure-as-Code (IaC) tools (, CloudFormation, Terraform, CDK) to manage reproducible environments.
Architect scalable ML infrastructure using AWS (, S3, Lambda, ECR, EC2, SageMaker).
Monitoring, Logging & Observability
Implement model and data monitoring with SageMaker Model Monitor, CloudWatch, or third-party tools.
Set up logging, alerts, and dashboards to ensure model health and performance.
Governance & Compliance
Manage model registries, lineage tracking, and audit logging to support reproducibility and regulatory compliance.
Enable version control and approval workflows for ML assets.
Collaboration & Enablement
Work closely with data scientists, ML engineers, and DevOps teams to integrate ML workflows into existing infrastructure.
Educate and mentor cross-functional teams on MLOps best practices and AWS ML tooling.
Aws Cloud Specialist • Hyderabad, Andhra Pradesh, India