Job Summary
The Java AI Solutions Engineer will join the Technology team to identify high-value opportunities for AI and automation, design proportionate solutions, and deliver secure, observable systems into production. The role combines hands-on engineering, business process discovery, AI evaluation, production controls, and technical enablement in a global, regulated environment
Key Responsibilities
Partner with business and technology stakeholders to identify high-value AI and automation opportunities and define practical solutions.
Design, build, test, deploy, and maintain scalable, event-driven microservices and distributed applications.
Develop high-volume, low-latency systems, RESTful APIs, messaging integrations, and reusable service components.
Build and integrate AI and machine learning solutions, including agentic workflows, custom agents, tool integrations, and orchestration.
Define evaluation criteria, test sets, regression checks, and human-in-the-loop controls with business subject-matter experts.
Embed security, privacy, compliance, resiliency, and observability into solution design and delivery.
Support production stability through monitoring, troubleshooting, performance optimization, capacity planning, and cost management.
Collaborate across Product, QA, DevOps, Architecture, business, and control teams, and contribute reusable patterns and documentation.
Skill Requirements
Application engineering: Core Java, Spring Boot, microservices, distributed systems, design patterns, and event-driven architecture.
AI engineering: LLMs, agentic workflows, custom agents, tool calling, MCP, context management, retrieval, orchestration, evaluation, and Python.
Integration: High-volume messaging, event-driven frameworks, RESTful services, and enterprise middleware.
Cloud and DevOps: AWS or Azure, CI/CD, GitHub or Bitbucket, containerization, and deployment.
Data: SQL, data engineering fundamentals, SQL Server, DB2, Snowflake, and exposure to non-relational data stores.
Production engineering: Observability, tracing, performance optimization, failure analysis, security, privacy, governance, and production support.
Other Requirements
Bachelor’s or master’s degree in computer science or a related field, or equivalent practical experience.
4 to 7 years of experience delivering production-grade distributed applications and AI or agentic solutions.
Strong analytical and problem-solving skills, with sound judgment across engineering, operational risk, and delivery trade-offs.
Clear communication and effective collaboration across business, engineering, architecture, and control teams.