2 hours ago
Pune, IndiaStaff+
Responsibilities
- Lead the design and development of personalization and recommendation services and applications.
- Own technical architecture, engineering standards, and key technology decisions across model-serving, inference, and recommendation platforms.
- Design and build scalable, low-latency APIs, event-driven services, and integration layers for real-time and batch recommendation use cases.
- Partner with AI/ML Engineers and Data Engineers to deploy machine learning models and recommendation algorithms.
- Design and implement model serving, inference, feature access, and experimentation capabilities.
- Integrate LLM APIs and AI services into production applications.
- Ensure solutions are secure, observable, reliable, resilient, scalable, and performant.
- Drive architecture reviews, code reviews, technical mentorship, engineering standards, and continuous improvement.
- Provide technical leadership across teams and improve engineering productivity through automation and AI-assisted tools.
Requirements
- 10+ years of experience designing and building large-scale distributed backend systems on cloud-native platforms.
- 3+ years of experience leading a technical capability, project, or engineering team.
- Hands-on experience building and operating recommendation systems and personalization capabilities.
- Strong understanding of production ML integration, including model serving, inference, experimentation, feature access, observability, and operationalization.
- Experience building model-serving and inference integration layers for real-time and batch prediction workloads.
- Experience integrating and operationalizing LLM APIs and AI services in production applications.
- Experience with feature stores, vector databases, embedding-based retrieval, modern ML architectures, experimentation platforms, and A/B testing is advantageous or preferred.
- Expertise in backend engineering, API design, microservices, event-driven architectures, and scalable integration patterns using Java and Spring Boot.
- Experience with Redis or equivalent caching systems, Kafka or equivalent technologies, SQL and NoSQL databases, and distributed data management.
- Strong understanding of data-intensive systems, feature engineering concepts, high-throughput data processing, observability, reliability engineering, and performance optimization.
- Proven ability to make architectural decisions, influence technical direction, and mentor engineers.
- Experience using coding agents, LLMs, or AI-assisted engineering tools to improve software delivery and developer productivity.
- Experience in eCommerce, retail, or consumer digital products is strongly preferred.
Benefits
- PUMA provides equal opportunities and states that it does not tolerate harassment or discrimination.
Tech Stack
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