DevOps Lead

TECHJAYS

THE AI REIMAGINATION COMPANY

Position DevOps Lead

Experience 10+ Years

Location Coimbatore / Hybrid

Joining Immediate / ASAP

Primary SkillsDevOps, AWS or Azure, CI/CD, Kubernetes, Docker, Terraform, Linux, Git, Cloud Infrastructure, Infrastructure Automation, Monitoring & Observability

Preferred SkillsAI/ML Infrastructure Exposure, GenAI Applications, GitOps, Argo CD, Prometheus, Grafana, Python, Bash, PowerShell, Cloud Security, FinOps

About Techjays

At Techjays, we're helping businesses reimagine how they build, operate, and scale with AI at the core. We focus on developing secure, scalable, production-grade AI systems that solve complex business challenges and deliver measurable business outcomes.

Our team brings together experienced engineers and technology leaders from organizations such as Google, NetApp, ADP, Cognizant Consulting, and Capgemini. We work with global clients to build advanced AI-powered applications, enterprise platforms, and intelligent automation solutions.

We're looking for a DevOps Lead who can take ownership of cloud infrastructure, deployment automation, CI/CD pipelines, platform reliability, and engineering operations.

The ideal candidate will have deep hands-on expertise in either AWS or Azure, along with strong experience in Kubernetes, containerization, Infrastructure as Code, and production cloud environments.

You will lead the design and implementation of reliable infrastructure platforms, establish DevOps best practices, and work closely with software engineering and AI teams to support the deployment and operation of modern applications.

This role requires a strong hands-on cloud and DevOps specialist who can architect, implement, troubleshoot, and optimize production infrastructure. Prior exposure to AI/ML or AI-enabled applications is required, but specialized MLOps expertise is not mandatory.

Key Responsibilities

1. Cloud Infrastructure & Architecture

• Lead the design, implementation, and management of scalable, secure, and highly available cloud infrastructure.
• Own cloud infrastructure architecture and operations across AWS or Azure.
• Design and manage cloud environments covering compute, networking, storage, databases, and container platforms.
• Establish infrastructure standards, architecture patterns, and operational best practices.
• Manage cloud resource provisioning, configuration, scaling, and environment management.
• Ensure infrastructure meets requirements for availability, performance, security, and reliability.
• Drive infrastructure improvements through automation and continuous optimization.

2. DevOps & CI/CD Engineering

• Design, implement, and maintain CI/CD pipelines for application development and deployment.
• Manage multi-stage deployment workflows across development, testing, staging, and production environments.
• Implement automated build, testing, deployment, and release processes.
• Establish reusable pipeline templates, deployment standards, and approval workflows.
• Manage deployment strategies, including rolling, blue/green, and canary deployments.
• Implement rollback mechanisms and ensure reliable production releases.
• Integrate CI/CD workflows with source control, artifact repositories, and container registries.
• Continuously improve deployment efficiency, release reliability, and engineering productivity.

3. Containerization & Kubernetes Management

• Architect and manage containerized application environments using Docker and Kubernetes.
• Manage production Kubernetes clusters using Amazon EKS or Azure AKS.
• Implement Kubernetes configurations covering deployments, services, networking, secrets, storage, and resource management.
• Work with Helm charts for application packaging and deployment.
• Optimize container resource utilization, application scaling, and cluster performance.
• Troubleshoot complex container and Kubernetes-related production issues.
• Establish best practices for container security and workload reliability.

4. Infrastructure as Code & Automation

• Implement Infrastructure as Code using Terraform and cloud-native provisioning tools.
• Automate infrastructure provisioning, configuration management, and environment setup.
• Develop reusable infrastructure modules and deployment automation workflows.
• Use Python, Bash, or PowerShell to automate operational tasks and improve efficiency.
• Reduce manual intervention through standardization and automation.
• Maintain infrastructure documentation, configuration standards, and version-controlled deployment workflows.

5. Monitoring, Reliability & Production Operations

• Establish centralized monitoring, logging, alerting, and observability across cloud environments.
• Implement monitoring solutions using CloudWatch, Azure Monitor, Prometheus, Grafana, or equivalent platforms.
• Monitor application availability, infrastructure health, resource utilization, and system performance.
• Lead troubleshooting and resolution of complex production incidents.
• Conduct root cause analysis and implement permanent corrective actions.
• Establish operational practices for backup, disaster recovery, availability, and business continuity.
• Improve system reliability through proactive monitoring and performance optimization.

6. Cloud Security & Governance

• Implement cloud security best practices across infrastructure and application environments.
• Manage IAM, RBAC, access controls, service identities, and secrets management.
• Secure cloud networking, virtual networks, firewalls, and infrastructure access.
• Implement container security and infrastructure hardening practices.
• Ensure cloud environments follow organizational security and compliance requirements.
• Identify infrastructure vulnerabilities and coordinate remediation activities.
• Establish secure deployment and configuration management practices.

7. AI Infrastructure & Engineering Collaboration

• Work closely with AI/ML and software engineering teams to provide infrastructure for AI-powered applications.
• Support the deployment and operation of LLM-based applications, AI services, and AI-enabled platforms.
• Design cloud infrastructure capable of supporting AI workloads, including compute, storage, networking, and containerized services.
• Support the infrastructure requirements of AI applications across development, testing, and production environments.
• Collaborate with engineering teams to improve deployment reliability, scalability, and operational performance of AI-powered systems.
• Evaluate infrastructure technologies and tools that support evolving AI application requirements.

8. Technical Leadership & Team Development

• Provide technical leadership to DevOps engineers and guide implementation across projects.
• Establish engineering standards, infrastructure best practices, and operational procedures.
• Mentor team members on cloud architecture, automation, CI/CD, Kubernetes, and production operations.
• Lead technical discussions and make architecture decisions involving infrastructure and DevOps technologies.
• Collaborate with engineering, security, and product teams to align infrastructure capabilities with business requirements.
• Drive continuous improvement across DevOps processes, tools, and engineering practices.

Required Technical Skills & Experience

1. Cloud Platforms

• 10+ years of overall experience in DevOps, cloud infrastructure, or platform engineering.
• Strong hands-on expertise in at least one major cloud platform: AWS or Azure.
• Practical experience designing, deploying, and operating production-grade cloud infrastructure.

AWS: EC2, S3, IAM, VPC, EKS, Lambda, ECR, CloudWatch, and related infrastructure services.

Azure: Azure Virtual Machines, VNets, AKS, Azure DevOps, Azure Container Registry, Azure Monitor, Key Vault, and related infrastructure services.

Candidates do not need to be experts in both AWS and Azure. Deep expertise in either platform is the priority.

2. DevOps & CI/CD

• Strong experience designing and maintaining CI/CD pipelines.
• Hands-on experience with Azure DevOps, Jenkins, GitHub Actions, GitLab CI, or equivalent tools.
• Understanding of multi-stage pipelines, automated deployments, release approvals, and artifact management.
• Experience implementing deployment automation and rollback strategies.
• Strong understanding of Git workflows and version control practices.

3. Containers & Kubernetes

• Strong hands-on experience with Docker and Kubernetes.
• Practical experience managing EKS or AKS environments.
• Understanding of Kubernetes networking, scaling, storage, secrets, and workload management.
• Experience with Helm and container orchestration.
• Ability to troubleshoot production container and Kubernetes environments.

4. Infrastructure as Code & Scripting

• Strong experience with Terraform and Infrastructure as Code practices.
• Familiarity with CloudFormation, Bicep, ARM templates, or similar tools.
• Working knowledge of Python, Bash, or PowerShell for infrastructure automation.
• Strong understanding of Linux administration and system troubleshooting.

5. Monitoring & Reliability Engineering

• Experience implementing infrastructure and application monitoring.
• Familiarity with CloudWatch, Azure Monitor, Prometheus, Grafana, and centralized logging solutions.
• Understanding of incident management, root cause analysis, performance optimization, and system availability.
• Experience establishing alerting, operational dashboards, and reliability practices.

6. AI/ML Infrastructure Exposure

• Prior experience supporting AI/ML applications, Generative AI platforms, LLM applications, or AI-enabled products.
• Understanding of infrastructure requirements for deploying and operating AI-powered applications.
• Familiarity with containerized AI services, cloud-based model integrations, and scalable application environments.

Note: This role does not require specialized MLOps expertise or model development experience. Strong cloud and DevOps engineering fundamentals remain the primary requirement.

Preferred Skills

• Experience implementing GitOps workflows using Argo CD or similar tools.
• Experience with blue/green deployments, canary releases, and automated rollback mechanisms.
• Exposure to cloud security, enterprise networking, governance, and landing-zone architecture.
• Experience with cloud cost optimization and FinOps practices.
• Familiarity with AI/ML infrastructure and production GenAI applications.
• Experience with Python-based automation and infrastructure tooling.
• Relevant AWS, Azure, Kubernetes, or Terraform certifications.

What We're Looking For Beyond Skills

• Cloud Expertise: Deep technical specialization in AWS or Azure, with the ability to design and manage production infrastructure independently.
• Hands-on Engineering: Someone who actively builds, automates, troubleshoots, and improves infrastructure rather than focusing only on team management.
• Technical Ownership: Takes responsibility for infrastructure reliability, deployment quality, security, and operational performance.
• System Thinking: Understands how cloud infrastructure, applications, networking, databases, and deployment pipelines work together.
• Problem-Solving: Ability to diagnose complex production issues and implement long-term engineering solutions.
• Automation Mindset: Continuously identifies opportunities to eliminate manual work and improve operational efficiency.
• Technical Leadership: Can mentor engineers, establish best practices, and drive technical decisions.
• AI Readiness: Has practical exposure to supporting AI-powered applications and understands their infrastructure requirements.
• Collaboration: Works effectively with software engineering, AI/ML, security, and product teams.

Typical Technology Environment

AreaTechnologiesCloud PlatformsAWS, AzureCI/CDAzure DevOps, GitHub Actions, Jenkins, GitLab CIContainersDocker, Kubernetes, EKS, AKS, HelmInfrastructure as CodeTerraform, CloudFormation, Bicep, ARMGitOpsArgo CD, Git-based deployment workflowsMonitoring & ObservabilityCloudWatch, Azure Monitor, Prometheus, GrafanaAutomationPython, Bash, PowerShellOperating SystemsLinux, WindowsSecurityIAM, RBAC, Secrets Management, Network SecurityAI ExposureAI/ML Applications, GenAI, LLM Platforms, AI-enabled Products

What We Offer

• Competitive compensation aligned with experience and expertise.
• Coimbatore office-based work environment with hybrid flexibility.
• Opportunity to lead cloud infrastructure and DevOps engineering for global clients.
• Exposure to modern AWS/Azure, Kubernetes, automation, and AI engineering practices.
• Paid holidays and flexible time off.
• Medical insurance covering self and family, up to ₹4 lakh per person.
• Opportunities to influence infrastructure architecture and engineering practices.
• A culture that values clarity, integrity, ownership, and continuous growth.

Join Techjays

If you're passionate about building reliable cloud platforms, automating complex infrastructure, and leading DevOps engineering for modern AI-powered applications, we'd love to hear from you.

Skills

DevOps, Amazon Web Services (AWS), Windows Azure, Azure OpenAI, Artificial Intelligence (AI), Retrieval Augmented Generation (RAG), Anthropic Claude, Git, Linux/Unix, Powershell