Senior DevOps Engineer

Key Responsibilities

• Design, build, automate, and maintain DevOps platforms supporting AI, ML, Agentic, and traditional applications.
• Deploy, containerize, and manage applications using Docker and Kubernetes across both on-premises and cloud environments.
• Develop Infrastructure as Code (IaC) solutions for repeatable, scalable, and secure deployments.
• Build and maintain CI/CD pipelines that support rapid and reliable application releases.
• Define and implement DevOps standards, deployment frameworks, operational procedures, and platform best practices.
• Automate environment provisioning, application deployment, monitoring, and operational workflows.
• Deploy and support Python-based, Java-based applications and AI services.
• Build and manage workflows using tools such as n8n and related automation platforms.
• Troubleshoot deployment, performance, scalability, and reliability issues across distributed systems.
• Maintain highly available production environments with a focus on uptime, security, and performance.
• Develop operational runbooks, deployment documentation, and support procedures.
• Own and operate the end-to-end internal AI stack from model selection and integration to deployment and monitoring.
• Provide day-to-day support for production applications and infrastructure.
• Participate in release activities, maintenance windows, upgrades, and production deployments, including support during extended hours when required.
• Perform root cause analysis (RCA) and implement preventive measures to reduce recurring incidents.
• Monitor system health, performance, capacity, and availability.
• Collaborate with engineering teams to improve observability, alerting, and operational readiness.

Required Qualifications

• 5+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering (SRE), or Infrastructure Engineering roles.
• Proven experience supporting production applications in enterprise environments.
• Strong hands-on experience with: Kubernetes (on-premises and cloud), Docker containerization, Linux system administration, Python application deployment and operations, CI/CD pipeline development and automation, Infrastructure automation and configuration management.
• Experience deploying and supporting AI, ML, or Agentic applications in production.
• Experience operating GPU-based infrastructure for AI workloads.
• Strong understanding of MLOps concepts and practices.
• Experience with AI model deployment, monitoring, and operational support.
• Experience supporting production releases, maintenance activities, and incident management.