Staff, Software Engineer
Walmart · Bengaluru · 8+ yrs experience · Posted 2026-07-28
Tech stack: Azure, Cassandra, Elasticsearch, GCP, Go, Java, Kafka, Kotlin, Kubernetes, PostgreSQL, Python, Redis, SQL
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About the role
The International Personalization team is part of Walmart's International Tech organization, responsible for building the intelligent systems that make every customer interaction across Walmart's global markets feel uniquely relevant. We own the end-to-end personalization stack — from real-time feature pipelines and ML model serving to recommendation engines, search ranking, and experimentation platforms — powering experiences for hundreds of millions of customers across 19+ countries. Our team operates at the intersection of backend engineering, data science, and product innovation, tackling some of the most challenging problems in large-scale personalized commerce. We are a collaborative, globally distributed group of engineers and scientists who value technical excellence, data-driven decision-making, and a culture of continuous learning. If you're passionate about building systems that directly shape how customers discover and engage with products at massive scale, this is the team for you.
Responsibilities:
- Architect & build large-scale personalization systems — design real-time recommendation engines, feature stores, ML model serving pipelines, and experimentation platforms that handle millions of concurrent requests with strict latency SLAs (p99 < 100ms) across international markets
- Drive technical strategy & engineering excellence — set architectural direction for the P13N platform, define best practices across teams, conduct design reviews, and champion observability, reliability, and production excellence (SLOs, error budgets, chaos testing)
- Bridge ML and backend engineering — partner closely with Data Science and ML Engineering to productionize models, optimize inference pipelines, build A/B testing infrastructure, and close the loop between offline training and online serving
- Lead cross-functional initiatives — collaborate with Product, Platform, SRE, and partner teams across US and international markets to translate personalization strategy into technical roadmaps that drive measurable business outcomes
- Mentor & elevate the team — coach senior and mid-level engineers through pairing, design sessions, and thoughtful code reviews; raise the technical bar and foster a culture of learning, experimentation, and continuous improvement.
Qualifications:
- 8+ years of backend engineering experience — deep expertise in Java, Kotlin, Python, or Go, with a proven track record of designing, building, and operating large-scale distributed systems in production, ideally for personalization, recommendations, search ranking, or ML-serving workloads
- Strong distributed systems & cloud-native skills — hands-on experience with microservices architecture, Kubernetes, Kafka, cloud platforms (GCP/Azure), and databases across SQL and NoSQL paradigms (PostgreSQL, Cassandra, Redis, Elasticsearch)
- Personalization & ML platform exposure — familiarity with ML model serving (TensorFlow Serving, TorchServe), feature stores, real-time feature engineering, experimentation platforms, and recommendation/ranking algorithms
- Technical leadership at scale — demonstrated ability to lead architecture and design for complex, multi-team projects; influence without authority across engineering, data science, and product organizations
- Strong communication & mentorship mindset — clear, concise communicator (RFCs, design docs, executive summaries) with a genuine passion for growing engineers and building diverse, inclusive teams
Qualifications
- 8+ years of backend engineering experience — deep expertise in Java, Kotlin, Python, or Go, with a proven track record of designing, building, and operating large-scale distributed systems in production, ideally for personalization, recommendations, search ranking, or ML-serving workloads
- Strong distributed systems & cloud-native skills — hands-on experience with microservices architecture, Kubernetes, Kafka, cloud platforms (GCP/Azure), and databases across SQL and NoSQL paradigms (PostgreSQL Cassandra, Redis, Elasticsearch)
- Personalization & ML platform exposure — familiarity with ML model serving (TensorFlow Serving, TorchServe), feature stores, real-time feature engineering, experimentation platforms, and recommendation/ranking algorithms
- Technical leadership at scale — demonstrated ability to lead architecture and design for complex, multi-team projects; influence without authority across engineering, data science, and product organizations
- Strong communication & mentorship mindset — clear, concise communicator (RFCs, design docs, executive summaries) with a genuine passion for growing engineers and building diverse, inclusive teams
Responsibilities
- Architect & build large-scale personalization systems — design real-time recommendation engines, feature stores, ML model serving pipelines, and experimentation platforms that handle millions of concurrent requests with strict latency SLAs (p99 < 100ms) across international markets
- Drive technical strategy & engineering excellence — set architectural direction for the P13N platform, define best practices across teams, conduct design reviews, and champion observability, reliability, and production excellence (SLOs, error budgets, chaos testing)
- Bridge ML and backend engineering — partner closely with Data Science and ML Engineering to productionize models, optimize inference pipelines, build A/B testing infrastructure, and close the loop between offline training and online serving
- Lead cross-functional initiatives — collaborate with Product, Platform, SRE, and partner teams across US and international markets to translate personalization strategy into technical roadmaps that drive measurable business outcomes
- Mentor & elevate the team — coach senior and mid-level engineers through pairing, design sessions, and thoughtful code reviews; raise the technical bar and foster a culture of learning, experimentation, and continuous improvement.