AWS DevOps Engineer with AI

Job Title: AWS DevOps Engineer with AI

Location: Bengaluru, India

Experience: 7-10 years

Type: Full-time, Hybrid( 3 days WFO-Bengaluru)

Job Summary

We are looking for an AWS DevOps Engineer with experience in AI/ML to design, implement, and manage cloud infrastructure and deployment pipelines for AI-driven applications. The ideal candidate will have strong AWS expertise, CI/CD experience, and hands-on knowledge of deploying machine learning models in production environments.

Key Responsibilities

• Design, build, and maintain scalable infrastructure on AWS.Implement CI/CD pipelines for application and ML model deployments
• Deploy, monitor, and optimize AI/ML models in production
• Automate infrastructure using Infrastructure as Code (Terraform/CloudFormation)
• Manage containerized applications using Docker and Kubernetes (EKS)
• Monitor system performance using CloudWatch and other monitoring tools
• Ensure security best practices and cost optimization in AWS
• Collaborate with data scientists and development teams
• Architect and manage distributed, stateful systems in production environments
• Lead infrastructure migrations and zero-downtime cutovers across environments
• Monitor system performance, reliability, and SLAs using observability stacks (other than CloudWatch)

Required Skills

• Strong experience with AWS services (EC2, S3, RDS, Lambda, IAM, VPC)
• Experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, etc.)
• Hands-on experience with Docker and Kubernetes
• Experience with Infrastructure as Code (Terraform or CloudFormation)
• Knowledge of Python or Bash scripting
• Built agentic AI systems using frameworks like langgraph
• Experience operating distributed systems and stateful workloads at scale
• Kubernetes experience including StatefulSets, autoscaling, rolling upgrades
• Experience implementing blue/green deployments and rollback strategies
• Strong understanding of networking fundamentals (DNS, load balancing, TLS, VPC peering)

Preferred Qualifications

• Experience with MLOps practices
• Familiarity with monitoring tools (Prometheus, Grafana)
• Understanding of security and DevSecOps practices
• Experience tuning performance for latency-sensitive systems
• Experience with multi-region deployments and disaster recovery planning

Good to have (Optional):

• Experience managing high-volume data ingestion pipelines
• Experience working with search engines, vector databases, or large-scale data retrieval systems
• Experience handling data reindexing, cluster scaling, and system migrations