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

Job Description

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

\n

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

\n

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

\n

Role Summary

\n

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

\n

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).

\n

Key Responsibilities

\n

\n
• Work closely with customers and stakeholders to understand business goals, strategy, pain points, and operational needs.

\n

• Design technology solutions, AI/agent workflows, integrations, data requirements, security and risk requirements.

\n

• Build AI agents, copilots, applications, APIs, services, integration with enterprise systems, RAG, orchestration.

\n

• Design, generate and validate AI-generated code and engineering artifacts, and drive deployment of enterprise-grade applications, integrations, and AI-powered solutions.

\n

• Apply prompt engineering, context engineering, and AI enablement techniques to improve solution outcomes.

\n

• Customize SDD framework (GitHub SpecKit) to implement or enhance the application functionality.

\n

• 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.

\n

• Extract user flows, functional rules, boundary conditions, error paths, and integration points from the code using SDD framework.

\n

• Convert the extracted understanding into structured, reviewable specs using the SpecKit workflow (specification, clarify, plan, tasks, implement).

\n

\n

Additional Responsibilities

\n

\n
• Handle large or legacy code bases through chunking, indexing, dependency mapping, and context management so results stay accurate.

\n

• Review and validate AI-generated test designs, test cases, Playwright automation scripts, and production readiness.

\n

• Be able to generate functional, negative, boundary, and regression test cases from both specs and code-derived specs.

\n

• Generate and maintain test automation scripts using Page Object Model, reusable fixtures, and API-level tests alongside UI tests.

\n

• Tune prompts, templates, and guardrails so generated scripts follow correct coding standards (stable locators, test data handling, minimal flakiness).

\n

• Validate generated scripts by executing them and feeding failures back into generation.

\n

• Wire the flow into GitHub Actions or the client's CI/CD tooling: scan, spec generation, test generation, execution, reporting.

\n

• Work embedded with client teams to assess their applications, documentation state, and automation maturity.

\n

• Run pilots, demos, and enablement sessions, and document how to use and extend the solution.

\n

• Help client QA and developers review and approve AI-generated outputs, keeping a human in the loop.

\n

• Gather feedback, prioritize improvements, and escalate risks early.

\n

\n

Required Skills

\n

\n
• Spec-driven development: Working knowledge of GitHub Spec Kit or comparable approaches.

\n

• 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.

\n

• Primary Coding Language: Python.

\n

• 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.

\n

• Code analysis: Experience with static analysis or code parsing technologies (AST parsing, dependency analysis, or tools such as Tree-sitter, CodeQL, or SonarQube).

\n

• GitHub: Experience with GitHub workflows and GitHub Actions.

\n

• Communication: Strong ability to explain technical decisions to engineers and business stakeholders.

\n

\n

Professional Skills

\n

\n
• Strong problem-solving and analytical capabilities.

\n

• Customer-facing communication and consulting skills.

\n

• Ability to work independently in fast-paced environments.

\n

• Experience collaborating with cross-functional and distributed teams.

\n

• Strong ownership mindset with a focus on measurable business outcomes.

\n

\n