MLOps Engineer - Bangalore Location - Hybrid

Job Description: Senior DevOps / MLOps Engineer

About the Role

We are looking for a highly motivated Senior DevOps/MLOps Engineer with experience in designing, implementing, and managing scalable cloud infrastructure, CI/CD platforms, Agentic AI platform engineering and AI/ML deployment pipelines across clouds.

The ideal candidate will play a critical role in enabling enterprise application delivery by automating infrastructure provisioning, securing cloud environments, optimizing deployment strategies, and operationalizing AI/ML workloads. This role requires close collaboration with Software Engineering, Data Science, AI Engineering, Security, and Infrastructure teams to build highly available, resilient, and secure cloud-native platforms.

The candidate should have hands-on experience with Infrastructure as Code (IaC), container orchestration, cloud networking, monitoring, AI model deployment, and modern DevOps practices.

Key Responsibilities

• Design, implement, and manage highly available cloud infrastructure across AWS, Azure and GCP. Build secure multi-account, multi-subscription cloud environments. Manage VPCs/VNETs, Private Endpoints, Transit Gateway, VPN, ExpressRoute, Direct Connect, NAT Gateway, Load Balancers, and DNS.
• Build & Maintain CI/CD and GitOps Workflows and rollback processes
• Strategies to include : Blue-Green, Canary, Integrate security scanning within CI/CD pipelines etc
• Build and maintain Kubernetes platforms (EKS,AKS,ECS,ACA etc) supporting microservices. Configure Helm,Ingress Controllers, Service mesh, Horizontal Scaling, Cluster Scaling etc
• Implement reusable IaC with Terraform/Bicep/CloudFormation/ARM
• Implement enterprise monitoring and observability using Prometheus, Garafana, Cloudwatch, Monitor, Log Analytics, Open Telemetry etc
• Implement DevSecOps. Manage IAM, RBAC, Secrets management, key vaults etc. Integrate vulnerability scanning & compliance checks.
• Build scalable MLOps pipelines, manage GPU infrastructure, implement model monitoring & drift detection
• Deploy Agentic AI platforms (LLM orchestration, MCP, RAG, vector databases, AI gateways, model serving, evaluation, prompt/version management, multi-agent workflows, guardrails, GPU workloads).
• Collaboration : Mentor Junior Engineers, participate in architecture discussions, prepare technical documentation & runbooks, support hypercare to legacy infra and applications.

Required Skills

• AWS: EC2, EKS, ECS, Lambda, API Gateway , S3, IAM, VPC, RDS, DynamoDB,CloudWatch, Route53, Cloudfront, Aurora, Elasticache, SNS, SQS, EventBridge, Secrets & System Manager, Code Pipeline, Cloudformation, Bedrock etc
• Azure: AKS, VM, Container Apps, Container Registry, Monitor, App Insights, Log Analytics, SQL, Cosmos, Service Bus, Front Door, Functions, App Service, Azure DevOps, Entra ID, Key Vault, Azure ML, AI Foundry, ALB,
• DevOps : Docker, Kubernetes, Helm, GitHub Actions, Jenkins, ArgoCD, Terraform, Python, Bash, Powershell, SonarQube, Artifactory
• IaC : Terrform, CloudFormation, ARM, Bicep, Ansible
• Networking : DNS, HTTP/HTTPS, TLS, Reverse Proxy, VPN, CIDR, Routing, Load Balancing, Private Endpoints,
• AI Skills : The ideal candidate should have practical experience supporting AI platforms and modern GenAI workloads. MLflow, Kubeflow, Azure ML Pipelines, SageMaker Pipelines, Feature Stores, Model Registry, Experiment Tracking, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents / Agentic AI, Prompt orchestration, Model serving, Vector databases, AI gateways, Vector DBs, Multiagent Architecture, MCP, A2A Communication, Langraph, Semantic kernel, Autogen, Langchain, AI Gateways and Guardrails
• Release Automation : Infrastructure promotion across Dev/UAT/Pre-Prod/Production, Automated rollback, Smoke testing, Deployment approvals, Environment configuration management etc

Good To Have

• AWS/Azure certifications
• FinOps
• OPA
• Kafka/EventBridge/Service Bus
• Banking domain
• Platform engineering : Internal Developer Platforms (IDPs), Golden templates, Standardized deployment pipelines
• Hybrid cloud
• SRE

Educational Qualification

• Bachelors or Master’s degree in Computer Science, Information Technology, Engineering, AI/ML, Data Science, or a related field.