Job Description
Title : Associate Software Developer
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
3-4+ 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 California Residents click below for Privacy Notice:
