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Tiger Analytics is seeking a DevOps Architect to lead DevOps capability, mentor a small team, and collaborate with global teams including program leads, architects, and data scientists. The role focuses on automation, security, and scalable infrastructure across cloud platforms.
The ideal candidate will drive CI/CD adoption, manage deployments, and stay ahead of DevOps trends while enabling rapid, secure product delivery for clients worldwide.
Notice Period : Immediate -15 Days
Who we are
Tiger Analytics is a global AI and analytics consulting firm. With data and technology at the core of our solutions, our 3900+ tribe is solving problems that eventually impact the lives of millions globally. Our culture is modelled around expertise and respect with a team-first mindset. Headquartered in Silicon Valley, you’ll find our delivery centers across the globe and offices in multiple cities across India, the US, UK, Canada, and Singapore, including a substantial remote global workforce.
We’re Great Place to Work-Certified™. Working at Tiger Analytics, you’ll be at the heart of an AI revolution. You’ll work with teams that push the boundaries of what is possible and build solutions that energize and inspire.
About the role
As a DevOps Architect, you will be responsible for supporting the DevOps capability and managing small teams of DevOps engineers. In doing so, you will closely collaborate with program leads, technology architects and data scientists across global locations. On a typical workday, you could be doing one or more of the following:
Technical Skills
Infra as Code : Terraform, AWS CDK or Azure Biceps
Repository Management : Artifactory, Nexus, JFrog
Code Analysis : SonarQube
Security Tools : Fortify/Blackduck/Checkmarx
Monitoring / Security : Grafana, Prometheus
Database : MySQL, MongoDB, DynamoDB
GenAI & LLMs: LLM Fundamentals (architecture, tuning, limitations), Prompt Engineering, Agent Frameworks (LangChain, LangGraph, MCP), RAG (Retrieval-Augmented Generation) design and implementation,
API Integration (REST, GraphQL for LLM-driven workflows), Security & Governance for GenAI (prompt injection prevention, compliance, data governance)
What do we expect?