Lead Java with AI

Position Java + AI Lead
Location Phoenix, AZ - 3 days a week (Hybrid)

Hire Type FTE
Skills Java + AI Lead

L3 — Senior FDE (5–8 years)
Attribute L3 — Senior FDE
5–8 yrs

Mission / Role Summary
Owns an entire mid-size engagement or leads a major workstream on a strategic program (e.g., a
workflow-modernization (Pega→Camunda) or data-modernization domain). Technical face of
Persistent to the client at module level.

Key Responsibilities:
• Lead design and delivery of a program module; set code standards and module architecture
• Scope new workstreams; translate vague executive asks into concrete engineering plans
• Drive AIDLC adoption and measure Copilot / Speckit ROI on the module
• Lead design and PR reviews; mentor L1/L2 FDEs
• Own the client technical relationship at workstream/module level

Engineering Skills (Core)
• Java versions 8, 11, 17, 21, SOLID principles, OOP concepts, Design patterns, Functional
interfaces Java migration patterns
• Spring Framework – Core Concepts - Dependency Injection, MVC architecture, Controller
responsibilities, Transaction management, Core Spring annotations)
• Spring Boot - (Spring Boot features, Core annotations, Dependency Injection, Application
context, @SpringBootTest, Global exception handling, Configuration management, Multi-
environment setup, ORM best practices, Security basics, Multiple DB connections, Version
upgrades)
• Microservices Architecture (Monolith to microservices principles, Microservice patterns,
Saga pattern, Orchestration vs Choreography, API Gateway, Distributed data consistency,
Failure handling, Resilience strategies)
• REST APIs & Integration (REST lifecycle, Spring REST annotations, External API calls, Timeout
& fallback handling, API performance troubleshooting)
• Caching & Performance Optimization (Redis caching, @Cacheable, Cache eviction strategies,
Performance tuning)
• Security (Authentication & Authorization, Spring Security basics, Secure configuration
management, Certificates & credentials handling)

AIDLC & GenAI Capability
• Understands the AIDLC stages and where AI accelerates the SDLC
• LLM application basics: prompting, RAG concept, tool/function calling
• Effective day-to-day use of GitHub Copilot; writes simple eval cases
• Solid LLM application patterns — RAG, tool/function calling, MCP basics
• Spec-driven development; prompt and context-engineering fundamentals
• Designs RAG and agentic solutions; advanced context engineering
• MCP integrations across enterprise tools; defines eval strategy
• Drives measurable developer-productivity outcomes from AI tooling
• Java/Spring Boot, GitHub + Copilot, Maven, PostgreSQL, Docker, Jira/Confluence, Google Cloud Platform

Primary Tools & Stack fundamentals.
• Harness, GKE/Cloud Run, Kafka, SonarQube, Vertex AI basics; IBM Watsonx for Z (Track 3).
• Terraform, Camunda/Appian, Vertex AI, agentic frameworks (Agents + Skills, MCP).
Client / Stakeholder Engagement Trusted technical advisor at module level; runs client design reviews and demos.
Behavioral & Leadership
Expectations

• General, Behavioral & Problem Solving Project explanation, Recent work, Technical
challenges, Code reviews, Problem solving skills)
• Technical leadership and accountability for outcomes
• Mentoring and firm contribution (playbooks, reusable assets