Why Techjays

At Techjays, we're helping companies reimagine how they build, operate, and scale with AI at the core. We're part of the small percentage of companies globally building secure, production-grade AI systems that drive measurable business outcomes. Our team brings together engineers and leaders who have built and scaled products at organizations like Google, NetApp, ADP, Cognizant Consulting, and Capgemini.

We're looking for a DevOps Lead who can own cloud infrastructure, automation, CI/CD, reliability, and engineering practices for modern software and AI-enabled platforms. The ideal candidate will be deeply specialized in either AWS or Azure, with strong hands-on DevOps experience and practical exposure to AI workloads or AI-driven products.

Key Skills

Cloud Technologies (Expert): Strong hands-on expertise in either AWS or Azure, with deep practical experience designing, deploying, and operating production cloud environments.
AWS: EC2, S3, IAM, VPC, EKS, Lambda, ECR, CloudWatch, and related infrastructure services.
Azure: Azure VMs, VNets, AKS, Azure DevOps, Azure Container Registry, Azure Monitor, Key Vault, and related infrastructure services.
CI/CD: Azure DevOps, GitHub Actions, Jenkins, GitLab CI, or equivalent, including multi-stage pipelines, approvals, artifact management, and deployment automation.
Containers & Kubernetes: Docker, Kubernetes, AKS or EKS, Helm, scaling, networking, secrets, and production workload management.
Infrastructure as Code: Strong Terraform experience preferred. Exposure to CloudFormation, Bicep, ARM, or similar IaC tools is valuable.
Automation & Scripting: Strong Python, Bash, and/or PowerShell skills for infrastructure automation, deployment, and operational tooling.
Monitoring & Reliability: CloudWatch, Azure Monitor, Prometheus, Grafana, centralized logging, alerting, incident management, and performance optimization.
AI Exposure: Prior experience supporting AI/ML applications, GenAI platforms, AI services, LLM applications, or AI-enabled products. This is an exposure requirement, not an MLOps-specialist role.

Roles & Responsibilities

Lead the design, implementation, and operation of scalable, secure, and highly available cloud infrastructure.
Own DevOps strategy and technical direction across cloud infrastructure, CI/CD, automation, and production operations.
Build and maintain CI/CD pipelines for application and AI-enabled workloads across development, staging, and production.
Design and manage Kubernetes environments using AKS, EKS, Docker, and Helm.
Implement Infrastructure as Code using Terraform and relevant cloud-native tools.
Establish deployment standards, reusable pipeline templates, and automated release processes.
Implement monitoring, logging, alerting, and operational practices to maintain reliability and performance.
Work closely with engineering and AI teams to provide the infrastructure required to deploy and operate AI-enabled applications.
Support cloud environments used by AI/ML applications, including compute, networking, containers, storage, security, and observability.
Drive cloud security practices covering IAM/RBAC, secrets, networking, access controls, and infrastructure governance.
Troubleshoot complex production issues, lead root cause analysis (RCA), and implement permanent fixes through automation and engineering improvements.
Optimize infrastructure performance and cloud costs while maintaining reliability and scalability.
Evaluate and introduce DevOps tools and practices that improve engineering productivity and deployment quality.
Mentor DevOps engineers and provide technical leadership across projects and teams.

What We're Looking For Beyond Skills

Cloud Specialist: Deep hands-on expertise in either AWS or Azure. You do not need to be an expert in both.
Strong DevOps Foundation: Proven experience owning CI/CD, cloud infrastructure, containers, automation, and production operations.
AI Exposure: Experience working with or supporting AI/ML, GenAI, LLM, or AI-enabled applications and an understanding of their infrastructure needs.
Builder Mindset: Ability to design, implement, and improve systems rather than only manage existing environments.
Ownership: Willingness to take responsibility for reliability, delivery, security, and operational excellence.
Leadership: Ability to mentor engineers, drive technical decisions, and work effectively with engineering and product teams.
Location & Availability: Willingness to work onsite from the Coimbatore office and availability to join immediately or at short notice.

Good to Have

Experience supporting AI/ML, Generative AI, LLM or AI-agent based applications in production.
Experience with GitOps and Argo CD.
Experience with cloud security, governance, networking and enterprise landing zones.
Experience with blue/green, canary and automated rollback strategies.
Experience with Python-based automation or AI integrations.
Experience with FinOps and optimization of cloud infrastructure costs.
Relevant AWS, Azure or Kubernetes certifications.

What We Offer

Competitive compensation
Coimbatore office-based role with a strong engineering environment
Opportunity to lead production-grade cloud and AI-enabled platforms for global clients
Exposure to modern AWS/Azure, Kubernetes, DevOps and AI engineering practices
Paid holidays & flexible time off
Medical insurance (Self & Family, up to ₹4 Lakhs per person)
A culture that values clarity, integrity and continuous growth

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