Forward Deployed Engineer – Java + AI
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Location:
Phoenix, AZ | Hybrid
Job Description
- Bachelor's degree or Master’s Degree in Computer science, or a related field, with minimum 10+ years of experience.
- Java/Spring Boot, GitHub + Copilot, Maven, PostgreSQL, Docker, Jira/Confluence, GCP fundamentals.
- Harness, GKE/Cloud Run, Kafka, SonarQube, AI basics;
- Terraform, Camunda/Appian, Vertex AI, frameworks (Agents + Skills, MCP).
- 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)
- 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
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