Lead Java Developer

About LetitbexAI

LetitbexAI is a fast-growing AI-driven technology company focused on building intelligent, scalable, and enterprise-grade solutions. We work at the intersection of AI, data engineering, cloud, and business transformation, helping organizations unlock real value from artificial intelligence.

Position: Lead Java Developer

Experience: 8+ Years

Notice Period: Can be considered up to 15 Days

Location: Hybrid (3 days Work From Office) |

Level: Senior Developer/Software Developer

About The Role

We are looking for a Full Stack Developer to join our Catalogue Search Technology team building and scaling our automotive parts catalog platform on Google Cloud. You will own features end-to-end — from React-based search experiences to Java microservices powering product resolution, fitment matching, and catalog ingestion pipelines. 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, develop, and maintain React.js front-end applications for product catalog search, fitment lookup, and merchandising tools

Build and evolve Java/Spring Boot microservices for product resolution, catalog ingestion, and pricing APIs

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

Participate in CI/CD pipeline management, container orchestration (GKE/Kubernetes), and infrastructure-as-code (Terraform)

Collaborate with Product, Data Engineering, and Merchandising teams to translate business requirements into technical solutions

Must-Have Qualifications

Strong in at least one track (front-end OR back-end), competent in the other:

Front-End Track

4+ years with React.js (hooks, context, Redux/Zustand, TypeScript)

Experience building responsive, accessible SPAs with modern tooling (Vite/Webpack, Jest, React Testing Library)

REST/GraphQL API consumption, state management, and performance optimization

Back-End Track

4+ years with Java 11+ and Spring Boot microservices

RESTful API design, Hibernate/JPA, messaging (Kafka/Pub/Sub), and relational + NoSQL databases

Experience with distributed systems patterns: circuit breakers, retry, rate limiting, caching

Both Tracks

6+ years total software development experience

Solid understanding of DevOps fundamentals — CI/CD (GitHub Actions, Jenkins, Cloud Build), Docker, Kubernetes basics, GitOps workflows

Working knowledge of GCP services — Cloud Run, GKE, BigQuery, Cloud Storage, Dataflow, Pub/Sub

Experience with ETL/data pipeline design — batch and streaming ingestion, data transformation, schema evolution

Hands-on experience building observability — structured logging, metrics, distributed tracing, alerting dashboards (Grafana, Cloud Monitoring, Datadog)

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

Tech Stack

Layer

Technologies

Front-End

React.js, TypeScript, Redux, Material UI, Vite

Back-End

Java 17, Spring Boot, Spring Cloud, Hibernate

Data / ETL

BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Functions, Kafka

Search

Google Vertex Retail Search API, Elasticsearch

Cloud

GCP — GKE, Cloud Run, Cloud Storage, IAM, VPC

DevOps

GitHub Actions, Cloud Build, Docker, Kubernetes, Terraform

Observability

Grafana, Cloud Monitoring, Cloud Logging, PagerDuty, ServiceNow

GenAI

Vertex AI, Gemini API, LangChain, RAG pipelines

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