2 months 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
Categories
About PUMA
PUMA designs, manufactures, and sells athletic and sportstyle footwear, apparel, and accessories for athletes and lifestyle consumers worldwide. The public company (ETR: PUM), founded in 1948 and headquartered in Herzogenaurach, Germany, goes to market through wholesale, retail, and direct-to-consumer e-commerce, and owns the Cobra Golf and stichd brands. Its product lines span football, running, training, and everyday wear across more than 100 countries.