Java Technical Specialist - RESTful API, Spring Boot
HCLTech
Date: 1 day ago
City: Chennai, Tamil Nadu
Contract type: Full time
Chennai, Tamil Nadu
Job Summary
10+ years of enterprise software engineering experience with bachelor’s or master’s degree in computer science, Software Engineering, or a related technical discipline
Experience in solution architecture and SDLC transformation initiatives
Generative AI & Agent Technologies (2+ Years)
Hands-on experience building GenAI Agent systems using LangChain, LangGraph, CrewAI, AutoGen, MCP Protocol.
Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o
Experience implementing Retrieval-Augmented Generation (RAG) pipelines and vector stores (Pinecone, Weaviate, pgvector, Chroma)
Proficiency in prompt engineering techniques: Chain-of-Thought, ReAct, few-shot/zero-shot strategies
Exposure to cloud AI platforms: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI
Java / J2EE Technologies
Extensive hands-on experience with Java (11–21), Spring Boot, Spring Framework, and Jakarta EE / J2EE
Microservices development using REST/gRPC; experience with Spring framework
Messaging and event-driven architecture: Apache Kafka, RabbitMQ, or IBM MQ
Persistence: JPA/Hibernate, PostgreSQL, Oracle, Redis
DevOps tooling: Docker, Kubernetes, Maven/Gradle, Jenkins, or GitHub Actions
Solution Architecture & SDLC Transformation
Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance
Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices
Domain Experience
Experience in the life insurance domain: underwriting, claims processing, policy administration, or actuarial tooling
Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)
Soft Skills & Professional Competencies
Excellent written communication skills — ability to produce clear technical documents, architecture proposals, and executive summaries tailored to both technical and non-technical audiences
Strong verbal communication — able to articulate complex AI/engineering concepts confidently in meetings, workshops, and presentations
Stakeholder engagement — proven ability to work closely with business leaders, product owners, and cross-functional teams to align technical solutions with business goals
Collaborative team player — comfortable working in distributed, agile teams while also being self-driven as an individual contributor
Mentoring & knowledge sharing — willingness to coach junior engineers and contribute to a culture of continuous learning
Analytical thinking — structured problem-solver who can break down ambiguous challenges and propose pragmatic, scalable solutions
Adaptability — thrives in fast-moving environments where AI technologies and business priorities evolve rapidly
Key Responsibilities
Generative AI & Agent Technologies (2+ Years)
Hands-on experience building GenAI Agent systems using LangChain, LangGraph, CrewAI, AutoGen, MCP Protocol.
Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o
Experience implementing Retrieval-Augmented Generation (RAG) pipelines and vector stores (Pinecone, Weaviate, pgvector, Chroma)
Proficiency in prompt engineering techniques: Chain-of-Thought, ReAct, few-shot/zero-shot strategies
Exposure to cloud AI platforms: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI
Java / J2EE Technologies
Extensive hands-on experience with Java (11–21), Spring Boot, Spring Framework, and Jakarta EE / J2EE
Microservices development using REST/gRPC; experience with Spring framework
Messaging and event-driven architecture: Apache Kafka, RabbitMQ, or IBM MQ
Persistence: JPA/Hibernate, PostgreSQL, Oracle, Redis
DevOps tooling: Docker, Kubernetes, Maven/Gradle, Jenkins, or GitHub Actions
Solution Architecture & SDLC Transformation
Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance
Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices
Domain Experience
Experience in the life insurance domain: underwriting, claims processing, policy administration, or actuarial tooling
Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)
Soft Skills & Professional Competencies
Excellent written communication skills — ability to produce clear technical documents, architecture proposals, and executive summaries tailored to both technical and non-technical audiences
Strong verbal communication — able to articulate complex AI/engineering concepts confidently in meetings, workshops, and presentations
Stakeholder engagement — proven ability to work closely with business leaders, product owners, and cross-functional teams to align technical solutions with business goals
Collaborative team player — comfortable working in distributed, agile teams while also being self-driven as an individual contributor
Mentoring & knowledge sharing — willingness to coach junior engineers and contribute to a culture of continuous learning
Analytical thinking — structured problem-solver who can break down ambiguous challenges and propose pragmatic, scalable solutions
Adaptability — thrives in fast-moving environments where AI technologies and business priorities evolve rapidly
Skill Requirements
Generative AI & Agent Technologies (2+ Years)
Hands-on experience building GenAI Agent systems using LangChain, LangGraph, CrewAI, AutoGen, MCP Protocol.
Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o
Experience implementing Retrieval-Augmented Generation (RAG) pipelines and vector stores (Pinecone, Weaviate, pgvector, Chroma)
Proficiency in prompt engineering techniques: Chain-of-Thought, ReAct, few-shot/zero-shot strategies
Exposure to cloud AI platforms: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI
Java / J2EE Technologies
Extensive hands-on experience with Java (11–21), Spring Boot, Spring Framework, and Jakarta EE / J2EE
Microservices development using REST/gRPC; experience with Spring framework
Messaging and event-driven architecture: Apache Kafka, RabbitMQ, or IBM MQ
Persistence: JPA/Hibernate, PostgreSQL, Oracle, Redis
DevOps tooling: Docker, Kubernetes, Maven/Gradle, Jenkins, or GitHub Actions
Solution Architecture & SDLC Transformation
Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance
Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices
Domain Experience
Experience in the life insurance domain: underwriting, claims processing, policy administration, or actuarial tooling
Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)
Other Requirements
1.Relevant certifications in Java/J2EE, SpringBoot, or related technologies are a plus.
#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-
Job Summary
10+ years of enterprise software engineering experience with bachelor’s or master’s degree in computer science, Software Engineering, or a related technical discipline
Experience in solution architecture and SDLC transformation initiatives
Generative AI & Agent Technologies (2+ Years)
Hands-on experience building GenAI Agent systems using LangChain, LangGraph, CrewAI, AutoGen, MCP Protocol.
Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o
Experience implementing Retrieval-Augmented Generation (RAG) pipelines and vector stores (Pinecone, Weaviate, pgvector, Chroma)
Proficiency in prompt engineering techniques: Chain-of-Thought, ReAct, few-shot/zero-shot strategies
Exposure to cloud AI platforms: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI
Java / J2EE Technologies
Extensive hands-on experience with Java (11–21), Spring Boot, Spring Framework, and Jakarta EE / J2EE
Microservices development using REST/gRPC; experience with Spring framework
Messaging and event-driven architecture: Apache Kafka, RabbitMQ, or IBM MQ
Persistence: JPA/Hibernate, PostgreSQL, Oracle, Redis
DevOps tooling: Docker, Kubernetes, Maven/Gradle, Jenkins, or GitHub Actions
Solution Architecture & SDLC Transformation
Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance
Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices
Domain Experience
Experience in the life insurance domain: underwriting, claims processing, policy administration, or actuarial tooling
Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)
Soft Skills & Professional Competencies
Excellent written communication skills — ability to produce clear technical documents, architecture proposals, and executive summaries tailored to both technical and non-technical audiences
Strong verbal communication — able to articulate complex AI/engineering concepts confidently in meetings, workshops, and presentations
Stakeholder engagement — proven ability to work closely with business leaders, product owners, and cross-functional teams to align technical solutions with business goals
Collaborative team player — comfortable working in distributed, agile teams while also being self-driven as an individual contributor
Mentoring & knowledge sharing — willingness to coach junior engineers and contribute to a culture of continuous learning
Analytical thinking — structured problem-solver who can break down ambiguous challenges and propose pragmatic, scalable solutions
Adaptability — thrives in fast-moving environments where AI technologies and business priorities evolve rapidly
Key Responsibilities
Generative AI & Agent Technologies (2+ Years)
Hands-on experience building GenAI Agent systems using LangChain, LangGraph, CrewAI, AutoGen, MCP Protocol.
Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o
Experience implementing Retrieval-Augmented Generation (RAG) pipelines and vector stores (Pinecone, Weaviate, pgvector, Chroma)
Proficiency in prompt engineering techniques: Chain-of-Thought, ReAct, few-shot/zero-shot strategies
Exposure to cloud AI platforms: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI
Java / J2EE Technologies
Extensive hands-on experience with Java (11–21), Spring Boot, Spring Framework, and Jakarta EE / J2EE
Microservices development using REST/gRPC; experience with Spring framework
Messaging and event-driven architecture: Apache Kafka, RabbitMQ, or IBM MQ
Persistence: JPA/Hibernate, PostgreSQL, Oracle, Redis
DevOps tooling: Docker, Kubernetes, Maven/Gradle, Jenkins, or GitHub Actions
Solution Architecture & SDLC Transformation
Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance
Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices
Domain Experience
Experience in the life insurance domain: underwriting, claims processing, policy administration, or actuarial tooling
Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)
Soft Skills & Professional Competencies
Excellent written communication skills — ability to produce clear technical documents, architecture proposals, and executive summaries tailored to both technical and non-technical audiences
Strong verbal communication — able to articulate complex AI/engineering concepts confidently in meetings, workshops, and presentations
Stakeholder engagement — proven ability to work closely with business leaders, product owners, and cross-functional teams to align technical solutions with business goals
Collaborative team player — comfortable working in distributed, agile teams while also being self-driven as an individual contributor
Mentoring & knowledge sharing — willingness to coach junior engineers and contribute to a culture of continuous learning
Analytical thinking — structured problem-solver who can break down ambiguous challenges and propose pragmatic, scalable solutions
Adaptability — thrives in fast-moving environments where AI technologies and business priorities evolve rapidly
Skill Requirements
Generative AI & Agent Technologies (2+ Years)
Hands-on experience building GenAI Agent systems using LangChain, LangGraph, CrewAI, AutoGen, MCP Protocol.
Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o
Experience implementing Retrieval-Augmented Generation (RAG) pipelines and vector stores (Pinecone, Weaviate, pgvector, Chroma)
Proficiency in prompt engineering techniques: Chain-of-Thought, ReAct, few-shot/zero-shot strategies
Exposure to cloud AI platforms: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI
Java / J2EE Technologies
Extensive hands-on experience with Java (11–21), Spring Boot, Spring Framework, and Jakarta EE / J2EE
Microservices development using REST/gRPC; experience with Spring framework
Messaging and event-driven architecture: Apache Kafka, RabbitMQ, or IBM MQ
Persistence: JPA/Hibernate, PostgreSQL, Oracle, Redis
DevOps tooling: Docker, Kubernetes, Maven/Gradle, Jenkins, or GitHub Actions
Solution Architecture & SDLC Transformation
Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance
Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices
Domain Experience
Experience in the life insurance domain: underwriting, claims processing, policy administration, or actuarial tooling
Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)
Other Requirements
1.Relevant certifications in Java/J2EE, SpringBoot, or related technologies are a plus.
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