Position Overview

We are seeking a highly experienced Senior DevOps Engineer to design, build, and operate scalable cloud infrastructure with a primary focus on AWS. The ideal candidate has extensive experience managing complex distributed systems, deploying machine learning workloads, implementing secure and automated infrastructure, and supporting multi-service architectures in production environments.

This role requires a strong infrastructure engineering mindset, deep cloud expertise, and the ability to collaborate with software, data, and machine learning teams to deliver highly available and scalable platforms.

Key ResponsibilitiesCloud Infrastructure & Platform Engineering

• Design, implement, and manage cloud-native infrastructure primarily on AWS.
• Build and maintain highly available, fault-tolerant, and scalable production environments.
• Develop Infrastructure as Code (IaC) using tools such as Terraform or CloudFormation.
• Establish cloud governance, security, networking, and operational best practices.
• Optimize infrastructure costs while maintaining performance and reliability.

Kubernetes & Container Orchestration

• Design and operate production Kubernetes environments.
• Manage containerized applications and platform services across multiple environments.
• Implement autoscaling, service discovery, ingress routing, and workload isolation strategies.
• Optimize cluster performance, reliability, and resource utilization.

Machine Learning Infrastructure

• Deploy, manage, and scale machine learning workloads in production environments.
• Support GPU-based and CPU-based workloads for training and inference.
• Build deployment pipelines for ML models and AI services.
• Collaborate with ML engineers and data scientists to operationalize machine learning systems.
• Manage model-serving infrastructure and inference scaling requirements.

Networking & Multi-Service Architecture

• Design and maintain complex networking architectures across cloud environments.
• Configure and manage:
• Load balancers
• API gateways
• Service meshes
• Reverse proxies
• Traffic routing policies
• Support multi-service and microservice-based platforms.
• Implement secure communication between distributed services.

CI/CD & Automation

• Build and maintain robust CI/CD pipelines.
• Automate infrastructure provisioning, deployments, testing, and operational workflows.
• Implement deployment strategies including:
• Blue/green deployments
• Canary releases
• Rolling updates
• Improve engineering productivity through platform automation.

Reliability & Observability

• Implement monitoring, logging, tracing, and alerting solutions.
• Establish SLOs, SLIs, and operational metrics.
• Lead incident response and root-cause analysis activities.
• Continuously improve platform reliability and operational excellence.

Required QualificationsExperience

• 5+ years of DevOps, Platform Engineering, Site Reliability Engineering (SRE), or Infrastructure Engineering experience.
• Proven experience operating production environments at scale.
• Experience supporting mission-critical systems with high availability requirements.

AWS Expertise

Strong hands-on experience with AWS services including:

• EC2
• VPC
• IAM
• Route 53
• Application Load Balancer (ALB)
• Network Load Balancer (NLB)
• ECS and/or EKS
• S3
• RDS
• ElastiCache
• CloudWatch
• Secrets Manager
• Lambda (preferred)

Kubernetes & Containers

• Extensive Kubernetes production experience.
• Strong understanding of:
• Networking
• Ingress controllers
• Storage management
• Cluster operations
• Security policies
• Advanced Docker experience.

Infrastructure as Code

Experience with:

• Terraform (strongly preferred)
• CloudFormation
• Pulumi (nice to have)

CI/CD

Hands-on experience with one or more:

• GitHub Actions
• GitLab CI/CD
• Jenkins
• ArgoCD
• CircleCI

Networking

Strong understanding of:

• DNS
• TLS/SSL
• VPNs
• Routing
• Reverse proxies
• Service-to-service communication
• Network security architecture

Preferred QualificationsGoogle Cloud Platform Exposure

Experience with:

• GKE
• Cloud Run
• Compute Engine
• Cloud Storage
• IAM
• VPC Networking

Machine Learning Infrastructure

Experience deploying and operating:

• ML inference services
• GPU workloads
• Model-serving platforms
• MLOps workflows
• Vector databases
• LLM applications and AI infrastructure

Observability & Operations

Experience with:

• Prometheus
• Grafana
• OpenTelemetry
• ELK/OpenSearch
• Datadog
• New Relic

Security

• Cloud security best practices.
• IAM design and access controls.
• Secrets management.
• Vulnerability management and compliance frameworks.

Required Application Submission

Applicants should include:

• Resume/CV
• LinkedIn profile (optional)
• GitHub profile (if applicable)
• Description of the largest production infrastructure they have managed
• Summary of Kubernetes and AWS environments they have operated
• Details of machine learning or AI workloads they have deployed
• Examples of CI/CD pipelines and Infrastructure-as-Code projects they have implemented

Success Criteria

The successful candidate will:

• Architect and maintain highly reliable cloud infrastructure.
• Independently manage AWS-based production environments.
• Successfully deploy and operate complex ML and AI workloads.
• Design secure and scalable multi-service routing architectures.
• Drive automation, observability, and operational excellence.
• Serve as a technical leader for cloud infrastructure and platform engineering initiatives.
• Improve deployment velocity, system reliability, and infrastructure scalability across the organization.

Pay: ₹647,045.59 - ₹804,652.19 per year

Benefits:

• Health insurance
• Paid sick time
• Paid time off
• Provident Fund

Work Location: In person