Job Description

Full Stack Developer - Automotive Retail Catalog Platform

Location: Hyderabad, India

About the Role

We are building a next-generation Enterprise Search Platform that powers product discovery, fitment intelligence, and merchandising systems at scale (millions of SKUs, high-cardinality datasets, real-time streaming pipelines).

We are specifically looking for engineers with deep expertise in distributed systems, streaming architectures, and applied AI, who can operate at the intersection of:

• Search platforms
• Real-time data pipelines
• Distributed systems
• AI/LLM integration

This role sits at the intersection of modern cloud engineering, data-intensive retail systems, and emerging GenAI capabilities, Vertex Retails API for Commerce.

What You'll Do

• Design and implement high-performance product search and resolution systems using:
• Vertex AI Retail Search / Elasticsearch / custom retrieval engines
• Build:
• Attribute-heavy search models
• Fitment resolution logic (vehicle àpart mapping)
• High-cardinality indexing strategies
• Design and build event-driven, horizontally scalable systems using:
• Kafka / Pub/Sub / NATS
• Develop and optimize Cloud ETL pipelines on GCP (Dataflow, BigQuery, Cloud Functions, Pub/Sub) for large-scale product data processing (1.2M+ SKUs, millions of fitment records)
• Integrate with Google Vertex AI Retail Search API for product catalog indexing, search, and recommendations
• Implement observability practices — create dashboards (Grafana, Cloud Monitoring), alerts (PagerDuty, ServiceNow), and SLO-based monitoring for production services
• Apply GenAI/LLM capabilities to improve catalog data quality, product matching, and search relevance
• Build and optimize large-scale streaming pipelines: Apache Flink / Apache Beam / Dataflow
• Participate in CI/CD pipeline management, container orchestration (GKE/Kubernetes), and infrastructure-as-code (Terraform)
• Build and integrate:
• RAG pipelines
• Vector-based search systems
• AI-assisted product matching
• Collaborate with Product, Data Engineering, and Merchandising teams to translate business requirements into technical solutions

Must-Have Qualifications

• 2-4 years of hands-on software development experience
• Distributed Systems Depth (Mandatory)
• Strong hands-on experience in: Kafka / Pub/Sub / distributed messaging
• Experience building or debugging: Streaming pipelines (Flink / Beam / Spark Streaming)
• Applied AI / Modern AI Stack
• Experience with at least one: RAG pipelines, Vector DBs, MCP / agent frameworks
• Cloud-Native Systems
• Hands-on: Kubernetes, Docker, GKE, Cloud Run, Pub/Sub, BigQuery, Cloud Storage, Dataflow
• Deep expertise in Java 11+/17+ and Spring Boot, building scalable, fault-tolerant microservices
• Proven ability to implement distributed systems patterns, including: Circuit breakers, retries, rate limiting, back-pressure handling, Idempotency, eventual consistency, caching strategies
• Hands-on experience implementing:
• Structured logging, metrics, and distributed tracing
• Understanding of Generative AI concepts — LLM integration, prompt engineering, RAG patterns, vector search, or AI-assisted development workflows

Good-to-Have Qualifications

• Automotive retail / parts catalog domain knowledge — ACES/PIES data standards, fitment data structures, base vehicle/engine base mapping, part terminology
• Experience with Google Vertex AI Retail Search API or similar product catalog search platforms (Elasticsearch)
• Familiarity with high-cardinality data modeling — attribute bucketing, product variant hierarchies (PRIMARY/VARIANT), multi-value attribute indexing
• Experience with Terraform for infrastructure provisioning on GCP
• Knowledge of ServiceNow integration for incident management workflows
• Exposure to BigQuery ML or Vertex AI for catalog enrichment / product classification
• Performance engineering — profiling, load testing (k6, Gatling), query optimization

What We Value

• Ownership mindset — you ship features, monitor them in production, and fix what breaks
• Pragmatic engineering — right-sized solutions over over-engineering
• Data fluency — comfort working with large datasets, complex schemas, and pipeline debugging
• Curiosity about GenAI — actively exploring how LLMs can improve developer productivity and product experiences
• Clear communication — ability to explain technical trade-offs to non-technical stakeholders

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