Database Compute -Sr Engineer

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.
  
  

• 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.
  
  

• 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