. AI Forward Deployed Engineer

Role

5. AI Forward Deployed Engineer

Level

Senior (2) and Mid / Senior (5)

Positions

7

Location

Offshore — Chennai, India

Years of Experience

Senior: 7+ years | Mid / Senior: 4–6 years

Role Summary

AI Forward Deployed Engineers (AI-FDEs) are the core delivery engine of the program. Operating in a forward-deployed model embedded within the product engineering organization, AI-FDEs combine full-stack Spring Boot + React implementation with AI-native delivery techniques — leveraging AI platform capabilities for dependency-aware code analysis, transformation scaffolding, generative test coverage, and deployment orchestration. AI-FDEs deliver platform foundation components (Form Designer FormRenderer, Workflow Engine state machine, multi-tenant routing service, Platform Shell, Admin Console) and rebuild the 17 security modules across three delivery waves on the 3-day epic lifecycle. Senior AI-FDEs additionally serve as tech leads, owning architectural quality for assigned sub-systems, running PR reviews, and mentoring Mid / Senior engineers.

Key Responsibilities

•      Deliver platform foundation components: React FormRenderer (JSON Schema draft-07, dynamic rendering, rich field library, grid / repeating sections), Workflow Engine state machine (step types FORM / APPROVAL / NOTIFY / BRANCH / PARALLEL, conditional routing, escalation, audit trail), Admin Console (tenant / role / data-group administration, searchable audit viewer)

•      Rebuild security modules end-to-end across three delivery waves in React 18 + Spring Boot 3.3.x with no legacy code reuse (Angular / VB.NET reference implementations consumed as functional specification only)

•      Implement the Spring Boot Tenant Routing Service using Java 21 virtual threads (JWT tenant extraction, request-scoped DataSource resolution, Hibernate-Filter tenant_id injection)

•      Co-design and implement versioned REST APIs with OpenAPI 3.0 (auto-generated via Springdoc), typed Spring ApplicationEvents, and webhook subscribers for external consumer integration

•      Execute the 3-day epic cadence: Day 1 — AI-generated specification + code generation + reconciliation against existing system behavior; Day 2 — validation, fixup, and integration testing against dependent services; Day 3 — full system test pass, observability configuration, code review, merge to main

•      Write and maintain unit tests (JUnit 5 + Mockito backend; React Testing Library + Jest frontend) meeting ≥80% / ≥70% coverage thresholds

•      Contribute to the Playwright E2E suite, accessibility assertions via axe-core, and integration / contract tests per endpoint

•      Perform code reviews — Senior AI-FDEs own PR-review accountability for pods of 3–4 Mid / Senior engineers; all engineers submit PRs by the daily cutoff for morning review

•      Participate in the daily live sync window (6:30–8:30 PM IST) for pairing, architecture alignment, and code walkthrough

•      Contribute to and consume Architecture Decision Records (ADRs) produced by the Technical Architect; Senior AI-FDEs co-author ADRs for sub-system decisions

•      Apply the Context Graph and dependency-impact analysis output when refactoring or extending existing modules to ensure zero regression in cross-module behavior

Required Qualifications

•      Senior: 7+ years full-stack development in Spring Boot + React (or strong Node.js + React with ≥2 years Spring Boot). Mid / Senior: 4–6 years equivalent

•      Strong Spring Boot 3.x, Java 17+ (Java 21 LTS preferred), JPA / Hibernate, Spring Security, REST API design

•      Strong React 18 LTS with TypeScript, functional components, hooks, React Hook Form, React Query / TanStack Query

•      Hands-on experience with JSON Schema renderers, dynamic form libraries, or comparable configuration-driven UI engines

•      Strong OpenAPI 3.0 / Springdoc, REST, and microservices patterns

•      Demonstrated unit + integration test discipline (JUnit 5, Mockito, Testcontainers, React Testing Library, Jest)

•      Apply AI-augmented software development practices as a daily working model — not an occasional accelerator

•      Demonstrated proficiency with AI-assisted development tools (Cursor, Claude Code, GitHub Copilot, or equivalent) including prompt engineering, context engineering, and tool-use patterns

•      Experience with AI-augmented workflows including dependency-graph code comprehension, legacy-to-modern code transformation, automated test generation, AI semantic-diff reconciliation, and AI-generated impact analysis

•      Deliver AI-ready code: semantic API schemas, typed event hooks, JSON-Schema-driven configuration, and agentic workflow integration points

•      Exposure to AI agent frameworks (LangChain, LangGraph, CrewAI, Semantic Kernel) or LLM-based code-analysis pipelines is a plus

•      Git + GitHub, PR-driven workflows, GitHub Actions

•      Strong written English for async standup, PR discussion, and pairing across time zones

•      Senior only: proven track record mentoring 3–4 engineers and leading technical design sessions; prior experience as a tech lead or staff engineer

Preferred Qualifications

•      Prior Angular + VB.NET experience (useful for reference-implementation analysis and reverse-engineering)

•      Experience replacing BPM / workflow engines (BP Logix, Activiti, Camunda, Flowable)

•      Exposure to multi-tenant SaaS or regulated-industry platforms (federal, healthcare, financial services)

•      AWS GovCloud or AWS Commercial familiarity (ECS / EKS, RDS, Cognito)

•      Experience building or consuming AI agent frameworks (LangChain, LangGraph, CrewAI, Semantic Kernel)

•      Familiarity with vector databases, RAG patterns, or LLM-based code analysis pipelines

•      Contributor to open-source React or Spring Boot ecosystems

Technical Skills

•      Backend: Spring Boot 3.3.x, Java 21 LTS (virtual threads), Spring Cloud Gateway, Spring Security, Hibernate 6, Flyway, Maven / Gradle

•      Frontend: React 18 LTS, TypeScript, React Hook Form, React Query / TanStack Query, Recharts / ECharts

•      Testing: JUnit 5, Mockito, Testcontainers, React Testing Library, Jest, Playwright (E2E contribution), axe-core

•      APIs: OpenAPI 3.0, Springdoc, Postman, REST, webhooks / typed Spring ApplicationEvents

•      AI-assisted development: GitHub Copilot, Cursor, Claude Code, context-engineering / prompt patterns, AI test generation, semantic-diff-based reconciliation

•      DevOps / CI: GitHub Actions, Docker, pipeline-as-code basics

•      Databases: SQL Server, PostgreSQL 16 (intermediate)

•      Observability: Micrometer, structured logging, CloudWatch basics