Senior Specialist - Devops Engineer

LTM; Maharashtra; Presencial; INR 3.500.000 - 7.000.000; Jornada completa

Descripción de la vacante

LTM is seeking an experienced cloud infrastructure leader to design, automate and operate cloud-native infrastructure for enterprise AI platforms. You will own CI/CD, IaC, observability and security across development, staging and production environments.

Responsibilities include deploying Kubernetes clusters, managing cloud services (AWS/Azure/GCP) and building scalable, cost-efficient platforms with strong monitoring and incident handling capabilities.

Formación

• Demonstrated experience with cloud-native infrastructure for AI platforms.
• Strong knowledge of CI/CD, IaC, observability, security and production operations.

Responsabilidades

• Design and manage cloud-native infrastructure for enterprise AI platforms.
• Build and maintain CI/CD pipelines for rapid and reliable software delivery.
• Automate infrastructure provisioning using Infrastructure as Code (IaC).
• Deploy and manage Kubernetes clusters, container platforms and cloud services.
• Implement monitoring, logging, tracing and operational dashboards.
• Collaborate with Engineering, AI, Data and Solution Architecture teams to support production deployments.
• Manage production incidents, root cause analysis and platform reliability.
• Optimize infrastructure cost, scalability and operational efficiency.
• Manage AWS, Azure or GCP environments.
• Deploy Docker containers and Kubernetes workloads.
• Implement autoscaling, load balancing and high availability.
• Manage secrets, certificates, IAM, networking and security policies.
• Implement disaster recovery, backup and business continuity strategies.
• Support enterprise deployments within customer cloud environments and VPCs.
• Implement centralized logging, distributed tracing and metrics collection.
• Build monitoring dashboards using Grafana, Prometheus, Datadog or equivalent.
• Define SLOs, SLAs and operational KPIs.
• Support load testing, stress testing, failover testing and capacity planning.
• Implement resilience patterns including retries, circuit breakers and graceful degradation.
• Demonstrated experience using AI coding assistants such as GitHub Copilot, Cursor, Windsurf, Claude Code or equivalent.
• Leverage AI to accelerate Infrastructure as Code, CI/CD pipeline development, YAML generation, scripting and documentation.
• Use AI to troubleshoot production incidents, optimize infrastructure and automate repetitive operational tasks.
• Review and validate AI-generated infrastructure code, scripts and deployment configurations before production use.
• Apply prompt engineering techniques to improve DevOps productivity and automation.
• Follow AI-assisted DevSecOps best practices while maintaining governance, security and compliance.
• Enterprise AI platforms, SaaS, Media & Entertainment, OTT, workflow automation or large-scale cloud-native applications.

Herramientas

Docker

Kubernetes

AWS

Azure

GCP

Terraform

GitHub Actions

Linux

Python

Prometheus

Grafana

Datadog

ELK

Networking

GitHub Copilot

Cursor

Windsurf

Claude Code

Descripción del empleo

Job Description – AI Platforms & Cloud Infrastructure

Experience: 10-15 years

Reports – Engineering Head

Role Summary

Design, automate and operate cloud-native infrastructure for enterprise AI platforms. Build highly available, secure and scalable deployment environments supporting AI applications, microservices, workflow orchestration and enterprise integrations. Own CI/CD, Infrastructure as Code, observability, security and production operations across development, staging and production environments.

Key Responsibilities

• Design and manage cloud-native infrastructure for enterprise AI platforms.
• Build and maintain CI/CD pipelines for rapid and reliable software delivery.
• Automate infrastructure provisioning using Infrastructure as Code (IaC).
• Deploy and manage Kubernetes clusters, container platforms and cloud services.
• Implement monitoring, logging, tracing and operational dashboards.
• Collaborate with Engineering, AI, Data and Solution Architecture teams to support production deployments.
• Manage production incidents, root cause analysis and platform reliability.
• Optimize infrastructure cost, scalability and operational efficiency.

Cloud & Platform Responsibilities

• Manage AWS, Azure or GCP environments.
• Deploy Docker containers and Kubernetes workloads.
• Implement autoscaling, load balancing and high availability.
• Manage secrets, certificates, IAM, networking and security policies.
• Implement disaster recovery, backup and business continuity strategies.
• Support enterprise deployments within customer cloud environments and VPCs.

Observability & Reliability

• Implement centralized logging, distributed tracing and metrics collection.
• Build monitoring dashboards using Grafana, Prometheus, Datadog or equivalent.
• Define SLOs, SLAs and operational KPIs.
• Support load testing, stress testing, failover testing and capacity planning.
• Implement resilience patterns including retries, circuit breakers and graceful degradation.

Required Technical Skills

• Docker, Kubernetes and container orchestration.
• AWS, Azure or GCP cloud platforms.
• Terraform, CloudFormation or equivalent Infrastructure as Code tools.
• GitHub Actions, Jenkins, GitLab CI or equivalent CI/CD platforms.
• Linux system administration and shell scripting.
• Python or Bash scripting.
• Prometheus, Grafana, ELK, Datadog or equivalent observability tools.
• Networking, IAM, security, API gateways and enterprise infrastructure.

AI-Driven Coding & Engineering Practices (Mandatory)

• Demonstrated experience using AI coding assistants such as GitHub Copilot, Cursor, Windsurf, Claude Code or equivalent.
• Leverage AI to accelerate Infrastructure as Code, CI/CD pipeline development, YAML generation, scripting and documentation.
• Use AI to troubleshoot production incidents, optimize infrastructure and automate repetitive operational tasks.
• Review and validate AI-generated infrastructure code, scripts and deployment configurations before production use.
• Apply prompt engineering techniques to improve DevOps productivity and automation.
• Follow AI-assisted DevSecOps best practices while maintaining governance, security and compliance.

Preferred Experience

• Enterprise AI platforms, SaaS, Media & Entertainment, OTT, workflow automation or large-scale cloud-native applications.
• Experience supporting AI workloads, GPU infrastructure or enterprise AI deployments is desirable.

Success Measures

• Highly available, secure and scalable production environments.
• Reliable CI/CD pipelines with rapid deployment cycles.
• Low production incident rates and fast recovery times.
• Optimized cloud cost, infrastructure performance and operational efficiency.
• Strong observability, automation and platform reliability.