Forward Deployed Engineer (FDE)

Role: Forward Deployed Engineer (FDE) - Java + AI Lead

Location: Austin, TX/Phoenix, AZ/ Dallas, TX (Hybrid)

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


Primary Tools & Stack
• Java/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).

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)