Senior Specialist - Architecture
Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Sep 2, 2026. LTM scores B on the Alion truth index.
Job Summary
Seeking a senior Java and Generative AI engineer to design develop and lead enterprisegrade AIenabled applications integrating Java microservices with advanced GenAI and LLMOps capabilities
Job Description
Design and develop scalable enterprise applications using Java J2EE Spring Boot Spring MVC and Java microservices Architect and implement Generative AI and Agentic AI solutions integrated with existing enterprise platforms Build AI agents for tasks such as code analysis defect triage productionlog analysis automated remediation and test generation Develop solutions leveraging Large Language Models LLMs RetrievalAugmented Generation RAG prompt engineering toolfunction calling and agentic workflows Integrate applications with OpenAIcompatible APIs and enterpriselocal LLM platforms Build orchestration workflows using frameworks like LangChain and custom agent frameworks Develop secure REST APIs and microservices integrated with enterprise AI services Utilize GitHub Copilot and AIassisted software development tools to enhance engineering productivity and adoption Design containerized secure AI applications compliant with regulated enterprise environments Work with databases and messaging platforms such as Oracle MySQL Neo4j and Kafka Apply LLMOps best practices for deployment monitoring and management of AI models in production environments Incorporate GenAI and LLMOps methodologies to optimize AI model lifecycle and operational efficiency Leverage expertise in GenAI and LLMOps to drive innovation and operational excellence in AI solution development
Roles and Responsibilities
Lead the architecture development integration and production deployment of AIenabled enterprise solutions combining Java microservices and Generative AI
Provide technical leadership architecture guidance code reviews and mentoring to development teams
Collaborate with business architecture security and platform teams to drive GenAI use cases from concept through production
Drive adoption of AIassisted software development capabilities across engineering teams
Ensure secure and scalable design of AI and microservicebased applications suitable for enterprise environments
Manage LLMOps processes including model lifecycle management monitoring and continuous improvement
Partner crossfunctionally to align AI solutions with business objectives and compliance requirements
Champion best practices in GenAI integration and LLMOps to enhance solution robustness and maintainability
Advocate for continuous learning and incorporation of emerging GenAI and LLMOps trends to maintain competitive advantage