What You'll Do: 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 Build, manage, and support comprehensive monitoring and observability for applications and AI model performance (drift, latency, accuracy) 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 Perform quality assurance for large-scale eCommerce backend search services and AI-powered features 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 What You'll Bring: 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) Working knowledge of 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 Additional Preferred 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) Contributions to open-source DevOps, AI/ML, or platform engineering projects are a strong plus Why Join Us Work at the intersection of AI and large-scale distributed systems - one of the most impactful domains in modern engineering Shape the future of search for hundreds of millions of Walmart customers globally Leverage cutting-edge GenAI tooling internally developed at Walmart (Wibey, ElementAI) Collaborate with world-class engineers on problems of enormous scale and complexity About Walmart Global Tech Imagine working in an environment where one line of code can make life easier for hundreds of millions of people. We work on big data problems, cutting-edge relevance algorithms from information retrieval, machine learning, and AI-powered ranking to deliver a high-availability, low-latency service that directly impacts business metrics.