Java Technical Lead - RESTful API, Spring Boot

Job Summary

1. Core Java Expertise (Kotlin is a plus)

Strong in Core Java, multithreading, collections, JVM concepts, and performance tuning

Ability to write clean, scalable, and secure enterprise-grade code

Kotlin knowledge is an added advantage for modern backend development

Tech Stack: Java 17+, Kotlin, Maven/Gradle, JUnit

Spring Boot & Microservices:

Strong hands-on experience with Spring Boot frameworks and microservices architecture

Knowledge of distributed systems, resiliency patterns, and event-driven architecture

Ability to design scalable and loosely coupled services

Key Skills: REST APIs, Kafka,

Good to have: Circuit Breaker, Saga, Docker, Kubernetes

Cloud-Native & Database Engineering:

Understanding of cloud-native principles and scalable backend design

Hands-on experience with SQL and NoSQL databases

Strong in database design patterns, performance optimization, caching, and scalability

Tech Stack: PostgreSQL, MongoDB, Redis

AI Ecosystem Knowledge:

Awareness of modern AI concepts including LLMs, RAG, AI Agents, and vector databases

Ability to integrate AI capabilities into enterprise applications securely and responsibly

Good Skills to have: Lang Chain, Prompt Engineering, Semantic Search

API Strategy & Enterprise Integration:

Strong understanding of API-first architecture and enterprise integration patterns

Ability to design secure, reusable, and scalable APIs with proper governance

Key Responsibilities

Design and contribute to workflow implementations Champion workflow orchestration best practices Build software as part of a nimble agile Team where you have every opportunity to make an impact on the bottom line and contribute to the architecture. Ensure our infrastructure is safely extensible, scalable, reliable and meets SLAs for both external and internal users. Ensure our solutions are testable, intuitive, and easy to maintain. Use state of the art tools for remote collaboration and developer happiness, i.e., IntelliJ Code With Me and Tuple Design, build, and operationalize Generative AI capabilities (LLM-powered services) with strong focus on security, reliability, and scalability. Implement Retrieval-Augmented Generation (RAG) patterns (ingestion, chunking, embeddings, vector search, reranking) to ground LLM outputs in enterprise knowledge. Develop and integrate LLM tool/function-calling ("agents") to orchestrate workflows across internal APIs and services while enforcing least-privilege access. Leverage Model Context Protocol (MCP) servers/tools (or equivalent patterns) to standardize how LLM applications access data sources and operational tools. Establish evaluation, monitoring, and guardrails for GenAI (prompting standards, hallucination,mitigation, PII controls, red-teaming, offline/online metrics).Participate in design and code reviews for key components and cross Enterprise initiatives.

Skill Requirements

• 8-13 years of software development experience, and preferably a Bachelor’s or master’s degree in computer science, computer engineering, or other technical discipline.

• Team player and a hands-on engineer.

• Experience mentoring and coaching junior engineers.

• Experience in designing and implementing highly scalable, low latency Java / Go based applications.

• Hands on experience in multi-threading programming.

• Hands-on experience building LLM-based applications using at least one major model/provider, and applying prompt engineering, structured outputs, and tool/function calling.

• Experience designing and implementing RAG systems, including document ingestion pipelines, embeddings, vector search, and relevance tuning.

• Experience integrating LLM applications with tools and enterprise systems (APIs, databases, queues) and familiarity with MCP concepts/servers for tool and context access.

• Understanding of GenAI security and risk controls (PII handling, prompt injection, data leakage), and experience with evaluation/observability of LLM systems.

• Basic high availability techniques and implementation knowledge.

• Practical knowledge of caching and distributed systems.

• Staying in touch with industry standards and current technologies is expected.

• Experience in profiling / performance analysis of applications.

• Core competencies in distributed technologies including Java, Spring, APIs (REST), JSON, XML, Kafka, JDBC, MongoDB, Postgres, NoSQL databases, Spring Boot, Spring Batch, JUnit, Jenkins, and Gradle/Maven.

• Experience with In-memory computing solutions is a big plus.

• Commitment to software practices of continuous Integration, automated/repeatable testing, and collaborative work environments.

• Ability to think abstractly and deal with ambiguous/under-defined problems.

• Ability to enable business capabilities through innovation.

• Demonstrated willingness to learn innovative technologies and takes pride in how fast they develop working software.

• Experience working with streaming solutions is highly desirable (preferably Apache Kafka and Kafka Streams).

• Hands-on experience in full-stack software development is desirable.

• Hands on experience in Big Data technologies including Python, Hadoop, and Spark is a plus

• Have excellent written and verbal communications skills.

• Familiarity with CI/CD pipelines and DevOps tools (Jenkins, GitLab).

Other Requirements

• Experience with container orchestration tools like Kubernetes and Docker.

• Previous experience with payment systems or real-time transaction platforms.

• Leadership experience in a fast-paced development environment.

• Experience in API development for fintech applications.

• Experience with vector databases and search stacks (e.g., Open Search/Elasticsearch, pgvector, Pinecone, Weaviate) and embedding lifecycle management.

• Experience building LLM agents with tool/function calling, including workflow orchestration, retries, and safe fallbacks.

• Experience creating/operating MCP servers (or similar abstractions) to expose enterprise data and actions to LLM applications with strong authentication/authorization.

• Familiarity with LLM evaluation techniques (golden datasets, human review workflows, automated scoring) and safety guardrails for regulated environments.