Forward Deployed Engineer

Hi

 

Our client is looking for a Forward Deployed Engineer  project in Phoenix, AZ below is the detailed requirement.

 

Job positing Title: Forward Deployed Engineer 
Location: phoenix, AZ

 

Required Skills: java full stack with angular experience ,  AI 

 

Job description:

•    Bachelor’s degree in related field
•    Java/Spring Boot, GitHub + Copilot, Maven, PostgreSQL, Docker, Jira/Confluence, Google Cloud Platform 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).
•    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