Surendirababu Janarthanan - Software Developer

Overview

Backend Java engineer (middle level) specializing in building REST services that integrate LLM tooling and vector-search document ingestion. The strongest proven skill is integrating document ingestion and vector storage with an LLM orchestration layer as implemented in DocumentProcessingService.java and IntelligenceAgentService.java. There is limited evidence of production-grade concerns such as migrations, retries/timeouts, auth/authorization, metrics, or test coverage.

Phone

Timeline

Lead Software Engineer · Lead

Wells Fargo · Full-Time

Jan 2026 to Present 10 Months In office

Architected an AI-powered document verification platform for Safe Deposit Box workflows, enabling secure ingestion, validation, and retrieval of customer and authorized-representative documents at scale. Built agentic AI and RAG pipelines using MCP and Pinecone with OpenAI embeddings, including classification, metadata extraction, and policy-driven routing. Implemented an event-driven microservices processing engine using Kafka and Elasticsearch, and strengthened multi-tenant security with OAuth2/JWT and RBAC.

Model Context Protocol

RAG

OpenAI

Pinecone

Apache Kafka

ElasticSearch

Associate · Middle

Cognizant Technology Solutions · Full-Time

Nov 2014 to Jul 2018 3 Years 8 Months Chennai In office

Built and maintained core microservices for the Titan platform, improving deployment speed and delivery flow. Optimized CI pipelines to reduce build failures and increase deployment frequency, supporting smoother releases to production systems.

CI/CD

Senior Systems Engineer · Senior

Infosys · Full-Time

Sep 2011 to Nov 2014 3 Years 2 Months Mumbai In office

Owned IMPS-related requirements and developed core service components for secure and reliable payment processing with NPCI integration. Contributed to end-to-end application development, including enhancements to modules aligned with business and operational needs.

Technical skills

Languages

3

Java

TypeScript

JavaScript

Java

5

Spring Boot

Spring Data JPA

Lombok

Spring MVC

SLF4J

DevOps

6

CI/CD

Kubernetes

GitHub

OpenShift

Splunk

Rest API

Databases

4

Redis

ElasticSearch

Pinecone

Apache Kafka

Other

11

Angular

OpenAI

Model Context Protocol

Ping Identity

AI Agents

AppDynamics

RAG

Embeddings

Platform Engineering

NLP

Twelve-Factor App

Middle Backend Developer Confidence: High API Engineer

Backend Java engineer (middle level) specializing in building REST services that integrate LLM tooling and vector-search document ingestion. The strongest proven skill is integrating document ingestion and vector storage with an LLM orchestration layer as implemented in DocumentProcessingService.java and IntelligenceAgentService.java. There is limited evidence of production-grade concerns such as migrations, retries/timeouts, auth/authorization, metrics, or test coverage.

API Design

3/10

How well APIs are designed

Basic REST API patterns are implemented with controllers and ResponseEntity usage, but there is no API versioning, idempotency handling, standardized error contract, or pagination conventions.

Evidence

Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/controller/DocumentController.java

Data Layer & Database

3/10

Working with databases

Uses Spring Data JPA repositories and service-layer lookups with Optional handling; lacks migration history, explicit transaction boundaries, isolation-level considerations, or documented N+1 mitigation.

Evidence

Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/service/tool/DatabaseToolService.java Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/config/DataInitializer.java

Scalability & Performance

2/10

Handling load and speed

Contains vector-store ingestion and chunking logic but no evidence of caching strategies, queue-based decoupling, connection/tuning configs or load-test driven optimizations.

Evidence

Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/service/document/DocumentProcessingService.java Surendirababu/Spring-AI-Intelligence-Hub/build.gradle

System Architecture

4/10

Overall system structure

Clear modular decomposition into controllers, service/tool layers and an agent orchestrator showing deliberate separation of concerns; architecture appears monolithic without inter-service contracts or service mesh concerns.

Evidence

Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/service/agent/IntelligenceAgentService.java Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/service/tool/DatabaseToolService.java

Security & Auth

2/10

Protecting data and access

Minimal security posture: basic input checks (file type) and no authentication/authorization, token lifecycle, secrets handling, or dependency auditing evident in code.

Evidence

Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/service/document/DocumentProcessingService.java Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/controller/DocumentController.java

Reliability & Observability

3/10

Stability and monitoring

Uses structured logging and some exception handling in controllers and services; lacks timeouts, retries/backoff, circuit breakers, metrics instrumentation, and graceful-shutdown patterns.

Evidence

Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/controller/DocumentController.java Surendirababu/Spring-AI-Intelligence-Hub/src/main/java/com/intelligencehub/service/agent/IntelligenceAgentService.java

Expertise

Backend AI & LLM· Middle

Databases & Vector Storage· Middle

Java· Middle

Industries

Commerce· Middle

Consumer Electronics· Middle

Technologies

Java· Middle

Rest API

Spring Boot

Apache Kafka· since 2026

Spring MVC

Spring Data JPA

SLF4J

Lombok

Recommendations

Develop RESTful backend endpoints and tool orchestration services that combine SQL lookups with vector search and LLM synthesis.
Build and harden document ingestion and vectorization pipelines, including robust chunking, metadata, batching, and backpressure handling.
Extend the project with production concerns: DB migrations, transaction boundaries, retries with backoff, timeouts, metrics and health/check endpoints.
Implement authentication/authorization and secure secret management for model/service access before production deployment.

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

neetcode-submissions Spring-AI-Intelligence-Hub

Job preferences

Work permits: United States, India
Citizenship: India
Employment type: Full-Time
Locations: Chennai