Senior Software Engineer, RAG and Agentic AI

NVIDIA NVIDIA · Semiconductors · Bangalore, India +1

Senior Software Engineer role focused on building and deploying production-grade RAG solutions and AI agents. The role involves designing and implementing scalable RAG architectures, developing AI agents with reasoning and multi-step execution capabilities, and orchestrating complex microservices deployments. Emphasis on optimizing RAG pipelines for accuracy, relevance, and performance, and driving continuous improvement through rigorous evaluation and collaboration.

What you'd actually do

  1. Plan, build and refine a GPU-accelerated, scalable, configurable Retrieval Augmented Generation (RAG) workflow and optimize it for accuracy, relevance, grounding and performance.
  2. Design and implement AI agents to enhance RAG pipeline which are capable of reasoning, planning, multi-step execution, and collaboration across tools and services
  3. Run fast, high-quality POCs on emerging agent and RAG architectures; harden successful patterns into generalized, reusable implementations and integrate them as part of production software.
  4. Build and deploy a disaggregated, end-to-end RAG pipeline using on-prem microservices architecture, orchestrating complex, multi-service deployments from local Docker environments to enterprise-scale Kubernetes clusters.
  5. Drive the continuous improvement of the pipelines by rigorously evaluating system accuracy, characterizing performance metrics across components, analyzing the data and recommending actionable strategic enhancements.

Skills

Required

  • Python
  • AI applications
  • LLM-powered AI applications
  • RAG
  • Agentic AI workflows
  • LLM design patterns
  • tool calling
  • prompt engineering
  • structured outputs
  • reasoning
  • agent frameworks
  • orchestration systems
  • LangGraph
  • LangChain
  • OpenAI Agents SDK
  • microservices
  • Docker
  • Helm
  • Kubernetes
  • end-to-end software lifecycle
  • release packaging
  • CI/CD pipelines

Nice to have

  • multi-agent systems
  • workflow orchestration engines
  • evaluation frameworks
  • MLOps pipelines
  • AI observability tooling
  • deploying AI models on data center, cloud, and embedded systems
  • AI coding agents

What the JD emphasized

  • deep expertise in Python, and AI applications
  • Hands-on experience building and deploying LLM-powered AI applications or RAG or Agentic AI workflows
  • Strong understanding of LLM design patterns, including tool calling, prompt engineering, structured outputs, reasoning
  • Experience with agent frameworks or orchestration systems such as LangGraph, LangChain, OpenAI Agents SDK, or similar
  • Have working experience with microservices, Docker, Helm, Kubernetes
  • Experience with end-to-end software lifecycle, release packaging, and CI/CD pipelines

Other signals

  • building production-grade RAG solutions
  • developing orchestration layers for AI agents
  • deploying end-to-end RAG pipelines