Forward Deployed Engineer / AI Developer

About the role

Forward Deployed Engineer —

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).

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)