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Java Springboot with AI

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Onsite

Java Springboot with AI

Tata Consultancy Services

Java Springboot with AI

Skills

SQL QueriesPythonJavaC#MicroservicesSpring BootMachine LearningAI

2 months ago

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Important information


Contract type:

Permanent

Location:

Chennai, Tamil Nadu, India

Work mode:

Onsite

Published on:

19 August 2026

What they need


Java Springboot with AI

Chennai / Hyderabad

5+ years of experience

***Face To Face Interview in Chennai and Hyderabad on 22nd of Aug***

  • Programming Skills

  • Strong proficiency in Python, Java, Springboot, Microservices, C#

  • SQL, Vector Databases

  • Hands-on experience with at least one machine learning framework and related AI development libraries.

  • LLM Application Development (OpenAI, Anthropic, Gemini)

  • Hands-on experience building production-grade applications using Large Language Models (LLMs).

  • Strong understanding of prompt engineering, context management, function calling, streaming, and evaluation techniques.

  • Familiarity with OpenAI API, Anthropic SDK, Gemini SDK, and their respective capabilities, limitations, and cost considerations.

  • RAG (Retrieval-Augmented Generation) Pipeline Design

  • Experience designing, implementing, and optimizing RAG-based solutions.

  • Proficiency with frameworks such as LangChain and vector databases.

  • Knowledge of chunking strategies, embedding model selection, retrieval optimization, and evaluation of retrieval quality.

  • Agentic AI & Multi-Agent Orchestration

  • Practical experience building autonomous AI agents and multi-agent systems using frameworks such as LangChain, LangGraph, and CrewAI.

  • Ability to design workflows where agents invoke tools, collaborate, delegate tasks, and execute multi-step processes.

  • Understanding of production challenges such as hallucinated tool calls, infinite loops, non-deterministic outputs, and reliability concerns.

  • MLOps & AI Observability

  • Experience implementing monitoring and observability for AI systems.

  • Ability to track model performance, output quality, latency, accuracy, and drift.

  • Hands-on experience with AI system monitoring, alerting, and continuous improvement practices.

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