AWS Devops MLOPS

Role Overview

We are seeking a highly skilled AWS MLOps Engineer to support the design, deployment, automation, monitoring, and operational management of cloud-native Machine Learning and Data Engineering platforms. The ideal candidate will have strong experience with AWS services, Infrastructure as Code (IaC), Kubernetes, CI/CD, platform operations, and production support. Experience supporting AI/ML workloads, SageMaker, Airflow/MWAA, and modern DevOps practices is highly desirable.

The role requires close collaboration with Data Engineering, ML Engineering, Platform Engineering, DevOps, and Operations teams to build scalable, secure, and reliable solutions across enterprise cloud environments.

Key ResponsibilitiesCloud Platform & MLOps

• Design, deploy, and support scalable AWS-based MLOps and data platforms.
• Manage and optimize AWS services including SageMaker, EKS, Lambda, S3, Glue, Athena, and Redshift.
• Support ML model deployment, lifecycle management, model monitoring, and operational governance.
• Collaborate with data scientists and engineering teams to operationalize ML workflows and automate model delivery pipelines.

Infrastructure & Automation

• Implement and manage Infrastructure as Code (IaC) using Terraform.
• Automate environment provisioning, configuration management, deployments, and operational workflows.
• Continuously improve platform reliability, scalability, and operational efficiency through automation.
• Maintain and enhance cloud infrastructure to meet business and operational requirements.

DevOps & Platform Engineering

• Design, implement, and maintain CI/CD pipelines for application, data, and ML deployments.
• Manage platform releases and deployment processes using GitHub, ArgoCD, Harness, or similar tools.
• Troubleshoot deployment issues and support production environments.
• Ensure high availability, resilience, and operational readiness of cloud platforms.

Monitoring, Observability & Support

• Implement monitoring, logging, alerting, and observability solutions.
• Proactively identify, troubleshoot, and resolve infrastructure and application issues.
• Support incident management, root cause analysis, and production issue resolution.
• Establish operational dashboards, health checks, and alerting mechanisms.

Security & Compliance

• Apply AWS security best practices across infrastructure and applications.
• Implement IAM, network security, encryption, secrets management, and compliance controls.
• Ensure platform governance, auditability, and adherence to enterprise security standards.
• Support vulnerability remediation and security reviews.

Roles & Responsibilities

Required Skills & ExperienceCloud & AWS

• Strong hands-on experience with AWS cloud services.
• Experience with:
  • Amazon EKS
  • Amazon SageMaker
  • AWS Lambda
  • Amazon S3
  • AWS IAM
  • Amazon MWAA (Managed Airflow)
  • AWS Glue
  • Amazon Athena
  • Amazon Redshift

Infrastructure as Code

• Terraform
• Infrastructure provisioning and automation
• Environment management and deployment automation
• Experience with Harness or similar deployment platforms

DevOps & Platform Engineering

• CI/CD pipeline implementation and management
• GitHub-based development workflows
• Kubernetes administration and operations
• Application deployment and release management
• Monitoring and observability practices
• Production support and incident handling

Programming & Scripting

• Strong Python programming skills
• Linux administration
• Shell scripting
• Basic to intermediate PySpark knowledge

Must-Have Skills

• AWS Cloud
• Terraform
• Kubernetes / Amazon EKS
• Python
• CI/CD pipelines
• Linux & Shell Scripting
• Monitoring & Alerting
• Production Support & Incident Management
• Cloud Security & Compliance

Good-to-Have Skills

• Amazon SageMaker
• PySpark
• ArgoCD
• GitHub Actions
• AWS Lambda, AWS Glue
• Amazon Athena, Amazon Redshift
• Infrastructure Observability Platforms
• Data Platform Operations

Preferred Qualifications

• Experience working in enterprise-scale MLOps or DataOps environments.
• Exposure to GenAI, LLMOps, and AI platform engineering.
• Experience with model deployment, model serving, and model monitoring frameworks.
• Knowledge of platform governance, operational excellence, and cloud-native architecture patterns.
• Experience supporting AI/ML platforms in production environments.
• Strong troubleshooting and root cause analysis skills.
• Ability to collaborate effectively with cross-functional teams including data scientists, software engineers, platform teams, and business stakeholders.

Desired Candidate Profile

The ideal candidate is a hands-on MLOps/Platform Engineer who combines AWS expertise, DevOps practices, automation mindset, and operational excellence. The individual should be comfortable managing production-grade ML platforms, driving automation initiatives, supporting cloud-native infrastructure, and ensuring reliable delivery of AI/ML workloads across enterprise environments.