Forward Deployed Engineer (Spec-Driven Test Generation and Automation)

Role: Forward Deployed Engineer (Spec-Driven Test Generation and Automation)

Location/mode: On-Site- Berkeley Height, NJ

Experience: 10 years in software engineering and solution engineering, 2–3 years in AI Engineering

Role Summary

Forward Deployed Engineer (FDE) who can connect business needs with technology solutions through AI-assisted engineering, automation, and contemporary software delivery practices.

In this role, you will partner directly with customers to turn their business requirements into scalable, well-engineered solutions. By applying AI-native engineering methods, Agentic SDC practices, and automation frameworks, you will speed up solution delivery while maintaining enterprise-grade quality and governance. The ideal candidate will design and deploy solutions using AI/SDC (spec-driven development).

Key Responsibilities

• Work closely with customers and stakeholders to understand business goals, strategy, pain points, and operational needs.
• Design technology solutions, AI/agent workflows, integrations, data requirements, security and risk requirements.
• Build AI agents, copilots, applications, APIs, services, integration with enterprise systems, RAG, orchestration.
• Design, generate and validate AI-generated code and engineering artifacts, and drive deployment of enterprise-grade applications, integrations, and AI-powered solutions.
• Apply prompt engineering, context engineering, and AI enablement techniques to improve solution outcomes.
• Customize SDD framework (GitHub SpecKit) to implement or enhance the application functionality.
• Build the code analysis capability that scans the application code base (UI components, routes, controllers/services, API definitions, data models, validation and business-rule logic) using SOD framework.
• Extract user flows, functional rules, boundary conditions, error paths, and integration points from the code using SDD framework.
• Convert the extracted understanding into structured, reviewable specs using the SpecKit workflow (specification, clarify, plan, tasks, implement).

Additional Responsibilities

• Handle large or legacy code bases through chunking, indexing, dependency mapping, and context management so results stay accurate.
• Review and validate AI-generated test designs, test cases, Playwright automation scripts, and production readiness.
• Be able to generate functional, negative, boundary, and regression test cases from both specs and code-derived specs.
• Generate and maintain test automation scripts using Page Object Model, reusable fixtures, and API-level tests alongside UI tests.
• Tune prompts, templates, and guardrails so generated scripts follow correct coding standards (stable locators, test data handling, minimal flakiness).
• Validate generated scripts by executing them and feeding failures back into generation.
• Wire the flow into GitHub Actions or the client's CI/CD tooling: scan, spec generation, test generation, execution, reporting.
• Work embedded with client teams to assess their applications, documentation state, and automation maturity.
• Run pilots, demos, and enablement sessions, and document how to use and extend the solution.
• Help client QA and developers review and approve AI-generated outputs, keeping a human in the loop.
• Gather feedback, prioritize improvements, and escalate risks early.

Required Skills

• Spec-driven development: Working knowledge of GitHub Spec Kit or comparable approaches.
• AI/LLMs: Practical experience with LLMs and AI coding assistants, including prompt engineering, structured outputs, context handling for large code bases, guardrails, and evaluating output quality.
• Primary Coding Language: Python.
• Code comprehension: Ability to read and reason about code in the client's stack (for example Java/Spring, .NET, Node.js or Python), including REST APIs and data models.
• Code analysis: Experience with static analysis or code parsing technologies (AST parsing, dependency analysis, or tools such as Tree-sitter, CodeQL, or SonarQube).
• GitHub: Experience with GitHub workflows and GitHub Actions.
• Communication: Strong ability to explain technical decisions to engineers and business stakeholders.

Professional Skills

• Strong problem-solving and analytical capabilities.
• Customer-facing communication and consulting skills.
• Ability to work independently in fast-paced environments.
• Experience collaborating with cross-functional and distributed teams.
• Strong ownership mindset with a focus on measurable business outcomes.