Staff, Software Engineer
Walmart · Bengaluru · Posted 2026-07-04
Tech stack: Azure, GCP, Java, Python, TypeScript
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About the role
We are a high‑impact engineering team with a diverse and modern technical skillset spanning Python, TypeScript, Java, and beyond. Our core expertise lies across the end‑to‑end Generative AI ecosystem, where we build and operate platforms that empower the broader developer community across the organization.
The team owns a rich and evolving portfolio of GenAI products and platforms, enabling teams to accelerate GenAI adoption across the entire product lifecycle—from experimentation and development to deployment, governance, and scale. We operate at the intersection of deep technology, platform thinking, and developer experience, making this team a hub for innovation, learning, and influence within the organization.
Responsibilities:
- Design, prototype, and productionize agentic AI solutions, including multi-agent workflows, tool use, memory, and orchestration patterns for real-world use cases.
- Implement GenAI applications using industry standard frameworks and related tooling—selecting the right approach based on scale, latency, and reliability needs.
- Integrate and operationalize LLMs, MCPs, tools/skills connectors, and evaluation/guardrail mechanisms to ensure quality, safety, and consistency in outputs.
- 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.
- Deliver cloud-native deployments with strong engineering hygiene, leveraging CI/CD, observability, and secure-by-design practices on Azure and/or GCP.
Qualifications:
- 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.
- Working knowledge of GenAI frameworks, and the ability to compare/choose frameworks pragmatically.
- 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).
- Strong communication and stakeholder management skills, paired with a self-driven learning mindset and proven ability to ramp up quickly in evolving technical areas.
Qualifications
- 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.
- Working knowledge of GenAI frameworks, and the ability to compare/choose frameworks pragmatically.
- 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).
- Strong communication and stakeholder management skills, paired with a self-driven learning mindset and proven ability to ramp up quickly in evolving technical areas.
Responsibilities
- Design, prototype, and productionize agentic AI solutions, including multi-agent workflows, tool use, memory, and orchestration patterns for real-world use cases.
- Implement GenAI applications using industry standard frameworks and related tooling—selecting the right approach based on scale, latency, and reliability needs.
- Integrate and operationalize LLMs, MCPs, tools/skills connectors, and evaluation/guardrail mechanisms to ensure quality, safety, and consistency in outputs.
- 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.
- Deliver cloud-native deployments with strong engineering hygiene, leveraging CI/CD, observability, and secure-by-design practices on Azure and/or GCP.