Forward Deployed Engineer

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.

Experience: 6-10 years in software and solution engineering, 2-3 years in AI engineering, 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.