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
5-7+ 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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