Senior, Software Engineer

Walmart · Retail · Bangalore, KA, India

Senior Software Engineer role focused on building and operating scalable, intelligent data platforms for analytics, automation, and AI-driven decision-making in the Fintech space. The role involves designing high-performance data systems, integrating Generative AI, and developing production-grade agentic systems. Responsibilities include architecting large-scale data systems, driving system design and execution for autonomous workflows, leading end-to-end productionization, defining engineering standards, guiding data and infrastructure decisions on GCP, enabling observability and traceability for AI agents, and collaborating with Product and Data Science teams on Gen AI initiatives.

What you'd actually do

  1. Architect Data & application Systems at Scale: Design large-scale, distributed data architectures capable of processing massive datasets, integrating AI-driven workflows, and supporting real-time and batch processing needs.
  2. Drive System Design and Execution: Architect, develop, and deploy application and data processing engine — enabling autonomous orchestration of data pipelines, analytics, and business workflows.
  3. Lead End-to-End Productionization : Own the full lifecycle — build, containerize, deploy, and maintain apps across environments using Kubernetes, Terraform, and CI/CD systems, spark.
  4. Define and Enforce Engineering Standards: Set best practices around code quality, observability, scalability, and operational readiness for AI and data systems.
  5. Guide Data and Infrastructure Decisions: Lead design discussions for data modeling, system integrations, and infrastructure architecture on Google Cloud Platform (GCP) and related technologies.

Skills

Required

  • Scala
  • Spark data processing
  • Scala or Java
  • Kubernetes
  • containerization
  • Terraform
  • CI/CD
  • automation
  • cloud-native operations
  • GCP
  • observability
  • monitoring
  • traceability
  • Airflow

Nice to have

  • Generative AI
  • retrieval-augmented generation (RAG) systems
  • large language models (LLM)

What the JD emphasized

  • production-grade agentic systems
  • autonomous orchestration
  • agentic interactions
  • Gen AI initiatives
  • LLMs

Other signals

  • integrating Generative AI capabilities
  • developing production-grade agentic systems
  • architect, develop, and deploy application and data processing engine — enabling autonomous orchestration of data pipelines, analytics, and business workflows
  • Establish end-to-end monitoring and observability frameworks (e.g., Prometheus, Grafana, OpenTelemetry) for data services, ensuring deep traceability of agentic interactions
  • Partner with Product, Data Science teams to align Gen AI initiatives with data engineering objectives
  • Own System-Level Decision Making: Evaluate new technologies, frameworks, and design patterns across LLMs and data infrastructure