Java Full Stack AI Developer

Job Description

•            Drive the adoption of embedded AI, moving beyond simple API calls to integrating local LLMs and vector databases into the application layer. Evangelize usage of AI tools to accelerate developer workflow and design products that do the same for the end user.

•            Lead architectural reviews to replace legacy patterns with modern, AI-augmented workflows. Identify bottlenecks in traditional software patterns and replace them with smarter, more efficient designs. Simulate impact between component and logic changes to database performance and cluster resource allocation.

•            Write clean, performant code while maintaining a high bar for automated testing and observability. Direct path solutioning, prioritizing stability and long-term maintainability.

•            Operate effectively with minimal oversight, taking ownership of the entire lifecycle.

Essential Technical Skills

• Backend - Java 17+, Spring Boot, Microservices architecture, Node.js
• Frontend - Full stack MERN, TypeScript, State management (Redux)
• AI/ML Implementation - MCP Development, Integration of LLMs (Gemini, Claude), Vector Databases, and RAG
• Cloud & DevOps - Kubernetes (EKS), Docker, CI/CD pipelines
• Data & Messaging - PostgreSQL, Redis, Kafka
• Security - OAuth2, OpenID Connect, and secure AI data handling practices

Experience and Education

•            8+ years of professional software development experience, with at least 3 years in a senior or lead engineering capacity and at least 3 end to end embedded AI production deployments.

•            Hands-on experience deploying machine learning models or AI-driven features into production software. Comfortable discussing the trade-offs between latency, cost, and accuracy in AI implementations.

•            Proven history of building and scaling application in production environments using Java and Spring.

•            Experience working in roles that require high adaptability, such as field engineering, solutions architecture, or rapid prototyping teams where you move between different technical environments.

•            Extensive work with containerized environments, specifically managing deployments and troubleshooting within Kubernetes clusters.

•            Bachelor’s or master’s degree in computer science, Software Engineering, or a related technical field. Equivalent professional experience in large-scale system design is also acceptable.