Senior, Software Engineer

About the role

Being part of the Search PTE-DevOps team at Walmart provides deep insight into the full lifecycle of a product — from content acquisition to being sold on Walmart.com. As a Senior Software Engineer in DevOps & AI Platform, you must support all systems and services to ensure high availability and reliability, while embracing AI-augmented workflows to accelerate engineering velocity.

Responsibilities

• Build, manage, and evolve QE & Release Automation frameworks, incorporating AI-assisted test generation and self-healing test capabilities
• Build and support Kubernetes-based containerization in production, including GPU-backed workloads for AI/ML inference
• Lead independently the investigation and resolution of high-impact search system and AI service incidents
• Maintain and improve automation pipelines supporting application build, release, and AI model deployment cycles (CI/CD + MLOps/LLMOps)
• Integrate AI coding assistants and GenAI tooling (e.g., Wibey, GitHub Copilot) into engineering workflows to accelerate development
• Design and implement AI-powered observability solutions using intelligent alerting, anomaly detection, and predictive incident management
• Collaborate with AI/ML teams to operationalize LLM-based features within search, including prompt pipeline management and vector search infrastructure
• Drive execution and lead medium- to large-scale projects from Dev to Ops, including AI/ML platform initiatives
• Analyze, design, and build frameworks using cutting-edge technology and AI tools to fulfill Operational Excellence
• Lead and independently handle high-impact, critical search system and AI service incidents
• Improve, optimize, and identify opportunities within the software development and AI deployment lifecycle (SDLC + MLOps)
• Provide engineering and QE teams with architectural guidance on solutions, automation frameworks, and AI integration patterns
• Work with product and engineering teams to review new functional and AI-driven requirements; develop comprehensive test plans and automate test cases — including AI model validation
• Write programs and scripts to automate testing and validation of search backend services and LLM/AI inference pipelines
• Expertise in WCNP, Concord, Looper, Python, Golang, and Java — with hands-on experience in AI/ML tooling, LLMOps, and GenAI platforms

Requirements

• Bachelor’s or Master’s Degree in Computer Science, Engineering, or related field
• 5+ years of experience building scalable eCommerce applications or distributed backend services
• 3+ years of industry experience in application releases, CI/CD pipelines, and distributed system testing
• Strong expertise in containerization and orchestration using Kubernetes (including multi-cluster and GPU-node management)
• 2+ years of programming experience in Python, Go, Java, and Shell scripting, with exposure to REST and gRPC API frameworks
• Experience with modern CI/CD platforms (e.g., Concord, GitHub Actions, Looper) and GitOps workflows (e.g., ArgoCD, Flux)
• Familiarity with AI/ML workflows: model serving, inference optimization, or LLM deployment pipelines
• Familiarity with observability stacks: OpenTelemetry, distributed tracing, log aggregation (e.g., Splunk, OpenObserve), and AI-assisted anomaly detection

Qualifications

• Experience with LLMOps and GenAI platforms: prompt engineering, RAG pipelines, vector databases (e.g., Pinecone, Weaviate, Elasticsearch KNN), and LLM evaluation frameworks
• Hands-on experience with AI coding assistants (e.g., Wibey, GitHub Copilot) and AI-augmented DevOps tooling
• Proficiency with WCNP (Walmart Cloud Native Platform) and cloud-native infrastructure on GCP or Azure
• Knowledge of eBPF-based observability tools (e.g., Cilium, Pixie) and advanced networking concepts (VIP, TCP, Envoy/Istio service mesh)
• Experience with GPU infrastructure management for AI workloads (CUDA, NVIDIA device plugins for Kubernetes)
• Familiarity with MLflow, Kubeflow, Ray, or similar MLOps platforms for experiment tracking and model lifecycle management
• Experience with performance and load testing tools (e.g., Gatling, k6, Locust) to measure server and client-side metrics
• Knowledge of AI safety and responsible AI practices in production environments (guardrails, content filtering, bias monitoring)

Skills

• Expertise in WCNP, Concord, Looper, Python, Golang, and Java — with hands-on experience in AI/ML tooling, LLMOps, and GenAI platforms

Benefits

• Competitive pay
• Performance-based bonus awards
• Health benefits
• Financial benefits
• Paid time off benefits
• Short-term and long-term disability
• Company discounts
• Military Leave Pay
• Adoption and surrogacy expense reimbursement

Pay

$117,000.00 - $234,000.00 annually

Schedule

Full-time