Job Description

Project Role :

Custom Software Engineer

Project Role Description :

Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.

Must have skills :

Machine Learning Operations

Good to have skills :

Python (Programming Language), AWS Architecture, AWS AI Services

Minimum

5

year(s) of experience is required

Educational Qualification :

15 years full time education



Summary We are seeking an experienced MLOps Engineer with strong expertise in building, automating, and maintaining end-to-end Machine Learning pipelines on AWS. The ideal candidate will have hands-on experience with AWS ML services, Infrastructure-as-Code, CI/CD for ML workflows, and scalable model deployment. This role requires strong collaboration, operational excellence, and the ability to support production-grade ML systems. ________________________________________ Roles & Responsibilities o Design and implement end-to-end ML pipelines--including data processing, model training, validation, deployment, and monitoring--using AWS services such as SageMaker, Lambda, Step Functions, ECS/ECR, and S3. o Develop Infrastructure-as-Code (IaC) using AWS CloudFormation, Terraform, or AWS CDK to automate deployments and resource provisioning. o Monitor and maintain model performance in production environments; build CI/CD workflows for ML models using CodePipeline, CodeBuild, and CodeDeploy. o Collaborate with data scientists, engineering teams, and DevOps to streamline model lifecycle management and ensure scalable ML operations. o Automate model retraining, rollback, and validation processes; establish robust testing and quality checks. o Manage containerization and orchestration using Docker, Kubernetes, EKS/ECS. o Document ML pipelines, operational procedures, and best practices to ensure long-term system maintainability. ________________________________________ Professional & Technical Skills o Hands-on experience with AWS AI/ML and compute services (SageMaker, Lambda, S3, IAM, Step Functions, CloudFormation, ECS/EKS). o Strong background in MLOps, including ML model lifecycle management, automation, monitoring, and production support. o Proficiency in CI/CD workflows and DevOps tooling (Git, CodePipeline, CodeBuild, CloudWatch, Prometheus). o Knowledge of security, compliance, and cloud governance best practices (e.g., HIPAA, GDPR). o Strong communication, documentation, and cross-functional collaboration skills. ________________________________________ Additional Information o Experience with large-scale distributed systems is a plus. o Familiarity with feature stores, model registries, or domain-specific ML frameworks is an advantage. o Ability to work in fast-paced environments and support multiple ML initiatives concurrently. o Certifications in AWS (e.g., AWS Certified Machine Learning - Specialty, DevOps Engineer - Professional) are preferred but not mandatory.




15 years full time education