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Overview Develop and maintain robust Ansible-based automation frameworks for provisioning, configuration, patching, and operational tasks Design and build reusable, modular Ansible roles and playbooks to ensure consistency, scalability, and maintainability Implement idempotent automation with proper error handling, conditional execution, and logging to ensure reliable production workflows Manage source control, branching strategies, and CI/CD integrations using Azure Repos and GitHub Optimize existing Ansible code for performance and efficiency by reducing unnecessary API calls, leveraging native modules, and minimizing reliance on raw Linux/command-based execution Design and implement enterprise-scale DevOps and automation architecture across cloud and infrastructure environments Integrate automation workflows with ServiceNow for ticket-driven execution, updates, and operational alignment Understand connection handling, authentication, and credential management for database-integrated applications Assist in implementing and validating database monitoring, alerting, and logging integrations Contribute to automation of routine database operational tasks where applicable (e.g., validation, status checks, reporting) Collaborate with cross-functional teams (infra, cloud, application, and service management teams) to drive automation adoption Implement alert-based automation for both compute and database environments, enabling automated response to events such as failures, threshold breaches, and health issues Familiarity with leveraging AI assistants, copilots, or automation insights to optimize DevOps workflows, troubleshooting, and operational dashboards Design and develop end-to-end Generative AI solutions using LLMs. Build and optimize RAG pipelines utilizing vector databases and enterprise knowledge sources. Fine-tune, evaluate, and deploy foundation models for domain-specific use cases. Develop scalable data pipelines for ingestion, transformation, embedding generation, and retrieval. Implement prompt engineering, agentic workflows, and AI orchestration frameworks. Responsibilities Accelerate infrastructure provisioning and operational workflows through automation. Reduce manual effort through AI-powered operational intelligence. Improve incident response using event-driven automation. Enable enterprise knowledge discovery through RAG-based AI assistants. Deliver scalable, secure, and production-ready Generative AI solutions that drive measurable business value. Qualifications Bachelor's in Data Science, Artificial Intelligence, Engineering, or related field Python, SQL Machine Learning & Deep Learning Generative AI & LLMs RAG Architecture LangChain, Lang Graph, LlamaIndexVector Databases (OpenSearch, Pinecone, Chroma DB, FAISS)AWS Bedrock, Sage Maker, Lambda, ECS/EKSMLOps & CI/CDGitHub, Azure DevOpsREST APIs and Microservices

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Hyderabad, India; Hyderabad, India

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First seen by us: Sep 9, 2026
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Job description

Overview Develop and maintain robust Ansible-based automation frameworks for provisioning, configuration, patching, and operational tasks Design and build reusable, modular Ansible roles and playbooks to ensure consistency, scalability, and maintainability Implement idempotent automation with proper error handling, conditional execution, and logging to ensure reliable production workflows Manage source control, branching strategies, and CI/CD integrations using Azure Repos and GitHub Optimize existing Ansible code for performance and efficiency by reducing unnecessary API calls, leveraging native modules, and minimizing reliance on raw Linux/command-based execution Design and implement enterprise-scale DevOps and automation architecture across cloud and infrastructure environments Integrate automation workflows with ServiceNow for ticket-driven execution, updates, and operational alignment Understand connection handling, authentication, and credential management for database-integrated applications Assist in implementing and validating database monitoring, alerting, and logging integrations Contribute to automation of routine database operational tasks where applicable (e.g., validation, status checks, reporting) Collaborate with cross-functional teams (infra, cloud, application, and service management teams) to drive automation adoption Implement alert-based automation for both compute and database environments, enabling automated response to events such as failures, threshold breaches, and health issues Familiarity with leveraging AI assistants, copilots, or automation insights to optimize DevOps workflows, troubleshooting, and operational dashboards Design and develop end-to-end Generative AI solutions using LLMs. Build and optimize RAG pipelines utilizing vector databases and enterprise knowledge sources. Fine-tune, evaluate, and deploy foundation models for domain-specific use cases. Develop scalable data pipelines for ingestion, transformation, embedding generation, and retrieval. Implement prompt engineering, agentic workflows, and AI orchestration frameworks. Responsibilities Accelerate infrastructure provisioning and operational workflows through automation. Reduce manual effort through AI-powered operational intelligence. Improve incident response using event-driven automation. Enable enterprise knowledge discovery through RAG-based AI assistants. Deliver scalable, secure, and production-ready Generative AI solutions that drive measurable business value. Qualifications Bachelor's in Data Science, Artificial Intelligence, Engineering, or related field Python, SQL Machine Learning & Deep Learning Generative AI & LLMs RAG Architecture LangChain, Lang Graph, LlamaIndexVector Databases (OpenSearch, Pinecone, Chroma DB, FAISS)AWS Bedrock, Sage Maker, Lambda, ECS/EKSMLOps & CI/CDGitHub, Azure DevOpsREST APIs and Microservices