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# Technical Lead - GenAI & Java Microservices

- **Company:** Spectraforce
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Automation of Tests, Code Coverage, Code Review, Computer Programming, Databases, Dataspaces, DevOps, Elasticsearch, Github, Junit, Python (Programming Language), PostgreSQL, Message Broker, Oracle (Applications), Productivity Software, RabbitMQ, Regression Testing, Mockito, Search Technologies, Systems Integration, Enterprise Data Management, Data Logging, GitHub Copilot, Retrieval-Augmented Generation, Large Language Models, Claude Code, Grafana, Prompt Engineering, Spring-boot, Hallucination Detection, Generative AI, Pgvector, Git Flow, Apache Kafka, Graphql, Splunk, OpenSearch, Jenkins, ChromaDB, Microservices
- **Published:** November 11, 2026
- **Apply:** http://leoforce.us/Careers/Spectraforce/JobDetails.html?jobid=839aa2a5-27d4-44f4-a14f-82ad56af1416&OrgId=1&UserId=6484

## About the Role

* Programming & Core Stack: Strong proficiency in Java 17+, Spring Boot, and Microservices architecture.
 * GenAI Frameworks & Integrations: Practical experience using Spring AI, LangChain4j, or Python AI service wrappers to interface with foundational models and build agentic/RAG workflows.
 * Vector Databases & Search: Working familiarity with vector search extensions and engines (e.g., pgvector, OpenSearch, ChromaDB, or Elasticsearch).
 * Data & Messaging: Practical experience with PostgreSQL or Oracle, alongside message brokers like Apache Kafka or RabbitMQ.
 * Testing & Modern Engineering: Strong foundation in unit, integration, and contract testing (JUnit, TestContainers), Git workflows, and CI/CD pipelines (Jenkins, GitHub Actions).
 * Leadership & Collaboration: Demonstrated success in leading a team of 4-8 engineers, conducting code reviews, estimating technical stories, and collaborating across distributed teams.

## Description

We are seeking a hands-on Technical Lead specializing in Java 17+ and Spring Boot microservices with practical experience implementing Generative AI features. You will bridge architecture and execution, guiding a team of engineers in building, optimizing, and deploying AI-augmented microservices, fine-tuning RAG retrieval systems, and ensuring clean, resilient enterprise code.

What are the top skills required for this role?

 * Modern Java & Microservices Core: Extensive hands-on expertise in Java 17+, Spring Boot 3.x, REST/Reactive APIs, and distributed microservices architecture.
 * Enterprise GenAI & LLM Integration: Practical experience with LLM orchestration, frameworks like Spring AI or LangChain4j, RAG pipelines, prompt engineering, and guardrails/security.
 * Vector Databases & Data Ecosystem: Proficiency in semantic search tools and vector stores (e.g., pgvector, OpenSearch), alongside messaging queues (Apache Kafka) and enterprise databases (PostgreSQL/Oracle)., * Hands-on Engineering & Delivery: Lead the technical implementation of Java-based microservices integrating GenAI capabilities, intelligent automation pipelines, and enterprise data connectors.
 * RAG & Prompt Engineering Execution: Build and maintain RAG pipelines, fine-tune retrieval algorithms, implement semantic vector lookups, and design structured prompt templates with robust validation.
 * Microservices Development: Write and review production-grade, high-performance code in Java 17+ and Spring Boot, implementing REST/GraphQL endpoints, Kafka consumers/producers, and asynchronous event processing.
 * Testing & Quality Assurance: Drive high code coverage with JUnit 5, Mockito, and TestContainers; establish automated testing for GenAI outputs (groundedness, hallucination detection, prompt regression testing).
 * Team Leadership & Sprint Governance: Conduct PR code reviews, mentor engineers on best practices (including developer productivity tools like GitHub Copilot/Claude Code), manage sprint deliverables, and resolve technical blockers.
 * Production Operations: Partner with SRE/DevOps to monitor service health, token usage, latency bottlenecks, and logging/observability using tools like Splunk, Grafana, or OpenTelemetry.