Java AI Developer (Full Stack)

We are seeking a Java AI Developer (Full Stack). This role requires strong hands-on engineering in enterprise Java/J2EE and modern React-based UI development, combined with experience building and operationalizing LLM-enabled solutions (including agentic architectures, prompt engineering, and model telemetry/observability). You will work in a Scaled Agile / Scrum environment as part of cross-functional squads delivering secure, compliant, production-grade software. [, Full-Stack Engineering (Core Delivery) Design, build, and enhance enterprise applications using Java/J2EE, Spring/Spring Boot, REST APIs, and common J2EE patterns (DAO/DTO, MVC, etc.). Develop modern, responsive web UIs using React (hooks, state management, component design), with strong focus on performance, accessibility, and maintainability. Build and integrate API layers; implement API security patterns (OAuth/JWT controls where applicable). DevOps / CI-CD / Quality Implement and maintain CI/CD pipelines (build, test, security scanning, deployment automation) using modern toolsets such as Jenkins and enterprise deployment tooling (e.g., uDeploy where applicable). Ensure high engineering quality through unit/integration testing, code reviews, static analysis, and automated gates. Partner with operations teams to support production readiness, monitoring, and incident triage using enterprise observability practices and toolchains. AI/LLM + Agentic Engineering (DTI AI-first expectations) Design and implement LLM-enabled capabilities (e.g., copilots, intelligent assistants, workflow automation) aligned to business and engineering use cases. Build agentic workflows (tool-using agents, planners, multi-step task execution, guardrails) and integrate them into enterprise applications. Apply strong prompt engineering practices: instruction design, context management, prompt templates, evaluation, and iteration to improve reliability. Implement model telemetry and operational monitoring: latency, token usage, quality signals, failure modes, prompt/response tracing, and feedback loops for continuous improvement. Work with governance/security stakeholders to ensure responsible AI usage and compliance expectations in a regulated banking context.

8+ years of software engineering experience with strong Java/J2EE foundation. Strong hands-on experience with Spring Boot / microservices, RESTful services, and enterprise integration patterns. Strong hands-on experience building UIs using React (modern JS/TypeScript, hooks, component architecture). Proven experience with modern CI/CD and DevOps practices (build automation, pipelines, deployments, quality gates). LLM exposure in real engineering contexts (RAG, tool/function calling, structured outputs, evaluation, safety patterns). Strong understanding of prompt engineering concepts and practical application. AI / Agentic Skills (Must Have / Strong Preference) Familiarity with ADK (Agent Development Kit), Playbook, or similar agentic frameworks (LangGraph, Semantic Kernel agent patterns, agent orchestration frameworks, etc.). Understanding of agentic architectures: planning, memory/context, tool invocation, retry/error handling, deterministic guardrails. Conceptual understanding of ML fundamentals (supervised/unsupervised learning, embeddings, evaluation metrics) and how they relate to LLM systems. Understanding of model telemetry/observability and how to operationalize LLM systems reliably in production. Preferred / Nice to Have Apigee experience (API proxies, policies, security, lifecycle management) strongly preferred. Experience with enterprise ticketing/monitoring ecosystems (e.g., ServiceNow/Jira and observability platforms) in production support contexts. Experience with secure SDLC practices in regulated environments (threat modeling basics, secrets management, vulnerability remediation). Exposure to internal developer productivity tooling (e.g., GitHub Copilot-style workflows) and AI-assisted SDLC improvements.