Role: Forward Deployed Engineer (FDE)
Engagement Type: Client‑embedded, delivery‑focused
Location/Mode: Barkeley Heights On‑Site
Experience: 6–10 years in software and solution engineering, 2–3 years in AI engineering
Role Summary
Implementation is seeking a driven Forward Deployed Engineer (FDE) who can bridge business needs with technology solutions through AI‑assisted engineering, automation, and modern software delivery practices.
In this role, you will partner directly with customers to translate business requirements into scalable, well‑engineered solutions. By applying AI‑native engineering, Spec‑driven design, and automation frameworks, you will accelerate delivery while maintaining enterprise‑grade quality and governance. The ideal candidate will design and deploy solutions using AIDLC (spec‑driven) development.
Key Responsibilities
- Collaborate with customers and stakeholders to understand business goals, strategies, and pain points.
- Design technology solutions, AI/Agent workflows, integrations, and data/security requirements.
- Build AI agents, copilots, applications, APIs, and services integrated with enterprise systems (RAG, orchestration).
- Generate and validate AI‑produced code and engineering artifacts; drive deployment of enterprise‑grade applications.
- Apply prompt and context engineering to enhance automation outcomes.
- Implement SDD framework (GitHub SpecKit) to improve or extend application functionalities.
- Develop code analysis capabilities to scan application codebases (UI, routes, controllers, APIs, data models).
- Extract user flows, functional rules, boundary conditions, and error paths using the SDD framework.
- Convert extracted insights into structured, reviewable specs using the SpecKit workflow (construction, specify, plan, task, implement).
- Manage large or legacy codebases through chunking, indexing, dependency mapping, and context management.
- Review and validate AI‑generated test designs, test cases, Playwright automation scripts, and quality assets.
- Generate functional, negative, boundary, and regression test cases from specs and code‑derived patterns.
- Design and maintain test automation scripts using Page Object Model, reusable fixtures, and AI‑run templates.
- Apply prompt engineering and guardrails to ensure generated scripts follow client standards (Playwright, Selenium).
- Validate generated scripts by executing and feeding back failures for improvement.
- Integrate workflows into GitHub Actions or client CI/CD pipelines for scan, spec generation, test generation, execution, and reporting.
- Work embedded with client teams to assess applications, documentation, and automation maturity.
- Conduct pilots, demos, and enablement sessions; document usage and extension guidelines.
- Support client QA and developers in reviewing and approving AI‑generated outputs with human oversight.
- Gather feedback, prioritize improvements, and escalate risks proactively.
Required Skills
- Spec‑driven development: working knowledge of GitHub SpecKit or similar frameworks.
- AI/LLMs: hands‑on experience with LLMs and AI coding assistants, including prompt design, structured outputs, context handling, and guardrails.
- Primary coding language: Python.
- Code comprehension: ability to read and reason about code in Java/Spring, .NET, NodeJS, or Python, including REST APIs and data models.
- Code analysis: experience with static analysis or code parsing (AST parsing, call graphs, control flow graphs) or tools such as Tree‑sitter, CodeQL, SonarQube.
- GitHub: experience with GitHub workflows and Actions.
- Communication: strong ability to explain technical decisions to engineers and business stakeholders.
Professional Skills
- Strong analytical and problem‑solving capabilities.
- Excellent customer‑facing communication and consulting skills.
- Ability to work independently in fast‑paced environments.
- Collaborative mindset across cross‑functional and distributed teams.
- Ownership‑driven approach focused on measurable business outcomes.
Good to Have
- Experience with agentic workflows or MCP‑based tooling.
- Knowledge of RAG or code‑indexing for large repositories.
- Experience with legacy modernization or reverse‑engineering undocumented systems.
- Familiarity with Azure DevOps, Jenkins, or similar CI/CD platforms.
- Exposure to financial services or payments applications.
- Experience with test management tools (Jira, Xray, Azure Test Plans).
- Experience migrating legacy automation (Selenium, UFT) to Playwright.
