Job Description

About Advance Auto Parts

Founded in Roanoke, VA in 1932, Advance Auto Parts is a leading automotive aftermarket retail parts provider that serves both professional installer and do-it-yourself Customers. As of July 13, 2019, Advance operated 4,912 stores and 150 Worldpac branches in the United States, Canada, Puerto Rico, and the U.S. Virgin Islands. The Company also serves 1,250 independently owned CARQUEST branded stores across these locations in addition to Mexico, the Bahamas, Turks, and Caicos and the British Virgin Islands. The company has a workforce of over 70,000 knowledgeable and experienced Team Members who are proud to provide outstanding service to their Customers, Communities, and each other every day.

About Advance India Innovation Center (AIIC):

We are continually innovating and seeking to elevate the Customer experience at each of our stores. For an organization of our size and reach, today, it has become more critical than ever, to identify synergies and build shared capabilities. The Advance India Innovation Center (AIIC), located in Hyderabad, is a step in this strategic direction that enables us to access a larger talent pool, unlock operational efficiencies and increase levels of collaboration.
 

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

• 7-10 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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