Job Title: AWS DevOps/MLOps Engineer (Agentic AI – AWS)
Experience: 6+ Years
Location: Remote
Budget: ₹1.10 LPM + GST
Job Summary
We are looking for an experienced AWS DevOps/MLOps Engineer to support an Agentic AI initiative. The ideal candidate should have strong hands-on experience with AWS infrastructure, DevOps automation, CI/CD, MLOps practices, and production deployment of AI/ML workloads.
The candidate will be responsible for designing, automating, deploying, and maintaining scalable infrastructure and machine learning workflows on AWS.
Key Responsibilities
- Design, provision, and manage scalable AWS infrastructure.
- Work with AWS services including:
- VPC
- EC2
- ECS / EKS
- S3
- IAM
- Lambda
- Step Functions
- Implement Infrastructure as Code using Terraform and/or AWS CloudFormation.
- Build and maintain CI/CD pipelines for AI/ML models, applications, and data pipelines.
- Implement end-to-end MLOps practices, including:
- Model and artifact versioning
- Automated training
- Automated testing
- Model deployment
- Model monitoring
- Work with AWS SageMaker for model training, deployment, monitoring, and drift detection.
- Automate ML and data workflows using tools such as Airflow, AWS Step Functions, or Kubeflow.
- Build scalable deployment pipelines for Agentic AI and machine learning applications.
- Implement infrastructure and application monitoring using:
- AWS CloudWatch
- Prometheus
- Grafana
- Troubleshoot infrastructure, deployment, and ML pipeline issues.
- Ensure AWS infrastructure follows security, scalability, reliability, and DevOps best practices.
Mandatory Skills
- 6+ years of relevant DevOps / Cloud / MLOps experience.
- Strong hands-on experience with AWS.
- Experience with EC2, VPC, IAM, S3, Lambda, ECS and/or EKS.
- Strong knowledge of CI/CD pipeline development and automation.
- Hands-on experience with Terraform and/or CloudFormation.
- Experience implementing MLOps workflows.
- Hands-on experience with AWS SageMaker.
- Experience with model training, deployment, monitoring, and drift detection.
- Experience automating data/ML workflows using Airflow, Step Functions, or Kubeflow.
- Good understanding of containerization and orchestration.
- Experience with observability and monitoring tools such as CloudWatch, Prometheus, and Grafana.
- Strong troubleshooting and problem-solving skills.
Preferred Skills
- Experience supporting Generative AI / Agentic AI workloads.
- Experience deploying AI/ML applications in production environments.
- Strong knowledge of Docker and Kubernetes.
- Experience managing scalable and secure cloud infrastructure.
- Understanding of model lifecycle management and production ML systems.
Ideal Candidate
We are looking for a hands-on AWS DevOps/MLOps Engineer who can independently build and manage cloud infrastructure, CI/CD pipelines, ML workflows, and production AI/ML deployments.
Candidates with strong experience in AWS, SageMaker, Terraform, Kubernetes, CI/CD, MLOps, Airflow/Step Functions, and monitoring will be preferred.
Interested candidates can share their updated resume at:
[email protected]
Email Subject: AWS DevOps/MLOps Engineer – Agentic AI
Pay: ₹80,000.00 - ₹90,000.00 per month
Work Location: Remote