(ind) Staff, Software Engineer

Walmart Walmart · Retail · Bangalore, KA, India

Staff Software Engineer focused on building Generative AI-enabled solutions and agentic architectures, integrating LLMs with tools and orchestration patterns for production-grade outcomes. The role involves full-stack development (Python/TypeScript) and cloud-native deployment on Azure/GCP, contributing to an internal platform for GenAI adoption.

What you'd actually do

  1. Design, prototype, and productionize agentic AI solutions, including multi-agent workflows, tool use, memory, and orchestration patterns for real-world use cases.
  2. Implement GenAI applications using industry standard frameworks and related tooling—selecting the right approach based on scale, latency, and reliability needs.
  3. Integrate and operationalize LLMs, MCPs, tools/skills connectors, and evaluation/guardrail mechanisms to ensure quality, safety, and consistency in outputs.
  4. Architect and Build end-to-end services and APIs using Full stack technologies like Python, TypeScript or other, collaborating with platform, product, and engineering stakeholders to translate requirements into solutions.
  5. Deliver cloud-native deployments with strong engineering hygiene, leveraging CI/CD, observability, and secure-by-design practices on Azure and/or GCP.

Skills

Required

  • Strong hands-on experience in designing and delivering GenAI/LLM-based systems (agents, tool calling, orchestration, prompt/response patterns, evaluation), ideally in production or near-production environments.
  • Solid software engineering fundamentals with System design experience in Full stack development, including system design, architecture design patterns, building services, integrations, and testable components.
  • Exposure to cloud-native engineering and CI/CD, with practical experience on Azure and/or GCP (containers, deployment pipelines, security, monitoring).

Nice to have

  • Working knowledge of GenAI frameworks, and the ability to compare/choose frameworks pragmatically.
  • Strong communication and stakeholder management skills, paired with a self-driven learning mindset and proven ability to ramp up quickly in evolving technical areas.

What the JD emphasized

  • agentic AI solutions
  • multi-agent workflows
  • tool use
  • memory
  • orchestration patterns
  • LLMs
  • evaluation/guardrail mechanisms
  • GenAI/LLM-based systems
  • agents
  • tool calling
  • orchestration
  • prompt/response patterns
  • evaluation

Other signals

  • design and build GenAI-enabled solutions
  • integrate LLMs with tools, skills, and orchestration patterns
  • production-grade outcomes
  • end-to-end Generative AI ecosystem
  • build and operate platforms that empower the broader developer community
  • agentic AI solutions
  • multi-agent workflows
  • tool use
  • memory
  • orchestration patterns
  • LLMs, MCPs, tools/skills connectors
  • evaluation/guardrail mechanisms
  • GenAI/LLM-based systems