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Tiger Analytics
DevOps - Senior Hiring - Multicloud
Tiger Analytics
Greater Chennai•Remote•12+ Yrs•3 hours ago
Job description
Remote
Overview
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.
Qualifications
- Identify the use cases which can be converted into reusable assets and with the help of engineering team convert that to assets
- Conduct the sessions in DevOps tools best practices, emerging technologies etc
- Help the team in RFP in solution DevOps approach, estimation, solution
- Independently drive the client projects in DevOps implementations
- Cloud : AWS, Azure, GCP
- Containers/Orchestration : Docker, Kubernetes, AKS, EKS
- Infra as Code : Terraform, AWS CDK or Azure Biceps
- CI/CD Tool : AWS Code Repo, Azure DevOps
- Repository Management : Artifactory, Nexus, JFrog
- Code Analysis : SonarQube
- Security Tools : Fortify/Blackduck/Checkmarx
- Monitoring / Security : Grafana, Prometheus
- Database : MySQL, MongoDB, DynamoDB
- Scripting/Languages : Shell, Python, Groovy, YAML
- GenAI & LLMs: LLM Fundamentals (architecture, tuning, limitations), Prompt Engineering, Agent Frameworks (LangChain, LangGraph, MCP), RAG (Retrieval-Augmented Generation) design and implementation,
- GenAI Cloud Services : AWS Bedrock, Azure OpenAI, Google Vertex AI API Integration (REST, GraphQL for LLM-driven workflows), Security & Governance for GenAI (prompt injection prevention, compliance, data governance)
- Drive DevOps adoption by implementing CI/CD tool sets and best practices for automation.
- Managing cloud AWS, Azure and GCP
- Hands on experience with containerization and orchestration platforms using Docker, and Kubernetes on Cloud (AKS, EKS)
- Automating tools/infra provisioning using Ansible, Terraform
- Automate provisioning and repetitive tasks using Terraform and Python.
- Provide mentorship and guidance to team members.
- LLM Fundamentals – Understanding how large language models work, their architecture, capabilities, and limitations.
- Prompt Engineering – Designing and iterating effective prompts for automation, coding assistance, operational queries, and troubleshooting scenarios.
- Agentic Framework – Hands-on experience with Model Context Protocol (MCP), LangGraph/ LangChain for building LLM-powered agents, multi-step reasoning workflows, and tool-augmented automation.
- RAG (Retrieval-Augmented Generation) – Designing and implementing solutions that combine LLMs with internal documentation, operational runbooks, or logs for contextual responses.
- GenAI Cloud Services – Hands-on experience with AWS Bedrock, Azure OpenAI Service, and Google Vertex AI Generative AI.
- API Consumption – Proficiency in integrating GenAI models into workflows using REST and GraphQL APIs - using Python FastAPI.
- Integration & Automation – Embedding Generative AI into CI/CD pipelines, ChatOps workflows, and ticketing systems for DevOps efficiency.
- Security & Governance for GenAI – Implementing measures to prevent prompt injection, ensuring compliance, managing sensitive data, and setting up governance frameworks.