FDE (Forward Deployed Engineer)/AI Developer - Onsite and Locals at Arizona

Forward Deployed Engineer —Lead design and delivery of a program module; set code standards and module architectureScope new workstreams; translate vague executive asks into concrete engineering plansDrive AIDLC adoption and measure Copilot / Speckit ROI on the moduleLead design and PR reviews; mentor L1/L2 FDEsOwn the client technical relationship at workstream/module levelEngineering Skills (Core)Java versions 8, 11, 17, 21, SOLID principles, OOP concepts, Design patterns, Functional interfaces Java migration patternsSpring 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 CapabilityUnderstands the AIDLC stages and where AI accelerates the SDLCLLM application basics: prompting, RAG concept, tool/function callingEffective day-to-day use of GitHub Copilot; writes simple eval casesSolid LLM application patterns — RAG, tool/function calling, MCP basicsSpec-driven development; prompt and context-engineering fundamentalsDesigns RAG and agentic solutions; advanced context engineeringMCP integrations across enterprise tools; defines eval strategyDrives measurable developer-productivity outcomes from AI toolingPrimary Tools & StackJava/Spring Boot, GitHub + Copilot, Maven, PostgreSQL, Docker, Jira/Confluence, GCP 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 EngagementTrusted technical advisor at module level; runs client design reviews and demos.Behavioral & Leadership ExpectationsGeneral, Behavioral & Problem Solving Project explanation, Recent work, Technical challenges, Code reviews, Problem solving skills)Technical leadership and accountability for outcomesMentoring and firm contribution (playbooks, reusable assets)