Responsibilities

Direct Responsibilities

Core Backend Engineering (Primary Focus)

- Architecture & Design: Lead the design and implementation of high-availability, distributed microservices using Java and Spring Boot.
- System Scalability: Develop and optimize complex backend systems to handle high-throughput transaction volumes and large-scale data processing.
- Code Excellence: Drive technical excellence through rigorous code reviews, unit testing (JUnit/Mockito), and adherence to SOLID principles and Design Patterns.
- API Management: Design and maintain secure, scalable RESTful and/or gRPC APIs.

DevOps Integration: Collaborate with DevOps teams to manage CI/CD pipelines, containerization (Docker/Kubernetes), and cloud deployment Contributing Responsibilities.

AI Agent & LLM Orchestration (Secondary Focus)

- Agentic Workflows: Design and deploy autonomous AI Agents capable of reasoning, tool-use (Function Calling), and multi-step task execution.
- Orchestration Frameworks: Implement AI logic using frameworks such as LangChain, LangGraph, or AutoGen.
- RAG Implementation: Build and optimize Retrieval-Augmented Generation (RAG) pipelines, integrating Vector Databases (e.g., Pinecone, Milvus, Weaviate)



with enterprise data.
- Prompt Engineering & Optimization: Develop sophisticated prompt templates and fine-tune agentic reasoning to minimize hallucinations and ensure deterministic outputs in production.

Integration: Seamlessly integrate LLM capabilities into existing Spring Boot microservices to automate complex business logic.

Primary (Backend):

- Languages: Expert-level Java (11/17/21).
- Frameworks: Spring Boot, Spring Cloud, Spring Security, Spring Data.
- Data Management: Proficiency in Relational Databases (PostgreSQL, Oracle) and NoSQL (MongoDB, Cassandra).
- Messaging/Streaming: Experience with Kafka, RabbitMQ, or similar event-driven architectures.
- Tools: Maven/Gradle, Git, Docker, Kubernetes.

Secondary (AI/ML):

- AI Orchestration: LangChain, LangGraph, or Haystack.
- LLM Knowledge: Deep understanding of OpenAI API, Anthropic, or open-source models (Llama, Mistral).
- Vector DBs: Experience with vector embeddings and similarity search.

Python (Complementary): Proficiency in Python for prototyping AI logic and interacting with AI ecosystems.

📌 Java Back End Developer with AI (Chennai)
🏢 Net Connect
📍 Chennai