Position Summary

We are looking for a Senior Software Engineer with 7–10 years of experience in designing and building scalable, cloud-native enterprise applications. The ideal candidate should possess strong expertise in Java, Spring Boot, Microservices, AWS, Kubernetes, Apache Kafka, and distributed systems, along with hands-on experience in AI-powered application development using Spring AI, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Vector Databases. The role involves designing highly available SaaS platforms, leading technical initiatives, mentoring engineers, and driving end-to-end product delivery.

Key Responsibilities

Design, develop, and maintain scalable cloud-native microservices using Java, Spring Boot, and REST APIs.

Build enterprise-grade SaaS applications following multi-tenant and event-driven architecture principles.

Create High-Level Design (HLD) and Low-Level Design (LLD) documents for complex business requirements.

Design and implement distributed systems with strong focus on scalability, resilience, security, and performance.

Develop asynchronous event-driven solutions using Apache Kafka, including topics, partitions, consumer groups, retries, and dead-letter queues.

Build and integrate RESTful APIs with frontend applications and third-party enterprise systems.

Develop AI-powered applications using Spring AI, Retrieval-Augmented Generation (RAG), LangChain, LangGraph, and OpenAI-based models.

Implement semantic search capabilities using Vector Databases such as Pinecone.

Integrate enterprise data sources using Model Context Protocol (MCP) to enable real-time AI responses.

Design conversational AI solutions including intelligent chatbots and workflow automation.

Deploy and manage applications on AWS using services such as EC2, EKS, S3, RDS, CloudWatch, IAM, VPC, SQS, and Secrets Manager.

Build and manage containerized applications using Docker and Kubernetes.

Improve platform observability through monitoring, logging, alerting, and automated recovery mechanisms.

Optimize application performance, database queries, caching strategies, and overall system reliability.

Design and implement CI/CD pipelines for automated build, testing, deployment, and release management.

Participate in production support, root cause analysis, incident resolution, and continuous platform improvements.

Write unit and integration tests using JUnit and Mockito while maintaining high code quality through SonarQube and TDD/BDD practices.

Mentor junior engineers, conduct design reviews, perform code reviews, and contribute to engineering best practices.

Collaborate with Product Managers, Architects, QA teams, and business stakeholders throughout the software development lifecycle.

Contribute to Agile ceremonies including sprint planning, backlog refinement, estimation, and retrospectives.

Required Skills

Backend Technologies

Java (8/11/17/21)

Spring Boot

Spring Framework

REST APIs

Microservices Architecture

Cloud & DevOps

AWS (EC2, EKS, S3, RDS, CloudWatch, IAM, VPC, SQS, Secrets Manager)

Docker

Kubernetes

CI/CD Pipelines

Git & GitHub

Event-Driven Architecture

Apache Kafka

RabbitMQ

Event Streaming

Consumer Groups

Dead Letter Queues (DLQ)

AI & Generative AI

Spring AI

Retrieval-Augmented Generation (RAG)

LangChain

LangGraph

OpenAI APIs

Pinecone Vector Database

Prompt Engineering

Model Context Protocol (MCP)

AI-powered Enterprise Applications

Databases

PostgreSQL

MongoDB

MySQL

Redis

SQL & NoSQL Database Design

Query Optimization

Caching Strategies

Frontend Exposure

Angular

TypeScript

React (working knowledge)

Testing & Quality

JUnit

Mockito

TDD

BDD

SonarQube

Architecture

High-Level Design (HLD)

Low-Level Design (LLD)

Distributed Systems

SaaS Architecture

Multi-tenant Applications

Secure & Scalable System Design

Experience

7–10 years of software development experience using Java and Spring Boot.

Strong experience building cloud-native enterprise applications on AWS.

Hands-on expertise in Microservices and Event-Driven Architecture.

Experience designing highly available distributed systems.

Practical experience with Kubernetes and containerized deployments.

Experience building AI-enabled enterprise applications using Spring AI, RAG, LangChain, LangGraph, Pinecone, and LLM integrations.

Experience developing secure REST APIs and integrating third-party enterprise platforms.

Experience supporting production systems, release management, and DevOps practices.

Proven experience mentoring engineers and leading technical initiatives.

Education - Any Bachelor's degree