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The Lead Forward Deployed Engineer (Lead FDE) is a hands-on technical team lead who guides a small pod of Forward Deployed Engineers through the delivery of high-impact solutions — from discovery to production — for Quanta’s operating companies. You will own day-to-day execution on a single active project at a time, setting technical direction, coordinating the work of 2–4 engineers, and personally contributing to the codebase alongside them. 

Unlike the Forward Deployed Engineering Manager, who shapes architecture across multiple initiatives, the Lead FDE is embedded in the details of one engagement: translating ambiguous business needs into shipped software, unblocking the team, and serving as the primary technical point of contact for stakeholders. Unlike an individual Forward Deployed Engineer, you are accountable not only for your own output but for the quality, velocity, and coherence of the whole pod’s work. 

This is a working lead role. You are expected to spend the majority of your time building — writing production code, prototyping with AI-assisted tooling, and pairing with engineers — while also planning, reviewing, mentoring, and communicating outcomes to business partners. 

• Lead a pod of Forward Deployed Engineers through end-to-end delivery of a single project, from problem framing to production hand-off. 

• Partner directly with business leaders, domain experts, and operating-unit stakeholders to understand needs and shape scope. 

• Translate ambiguous requirements into a concrete delivery plan with clear, testable milestones. 

• Write production-ready code as a hands-on contributor on the project, not just as a reviewer. 

• Make day-to-day technical decisions (frameworks, integrations, data models, deployment targets) and escalate architectural calls to the FDEM when needed. 

• Run code reviews, pairing sessions, and working sessions that raise the team’s technical bar. 

• Use AI-assisted development tools (GitHub Copilot, Claude, ChatGPT, and similar) to accelerate delivery and bring best practices back to the broader FDE team. 

• Identify risks, dependencies, and blockers early — and drive the team through them. 

• Deploy applications to production and coordinate post-deployment support, monitoring, and iteration with SMEs. 

• Mentor Forward Deployed Engineers through pairing, feedback, and targeted coaching on craft and consulting skills. 

• Document design decisions, reusable components, and lessons learned to support scale and knowledge-sharing across the FDE practice. 

• Represent the pod in status reviews, demos, and retrospectives with stakeholders and Data & AI leadership. 

• Stay current with emerging AI tools and development practices and pilot their adoption within your project. 

• Perform other duties as assigned. 

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field. 

• 8–12 years of experience in software development, systems integration, or technical consulting, with recent hands-on delivery experience. 

• At least 2 years in a technical lead, tech lead, or senior engineer capacity guiding the work of other engineers on a shared project. 

• Advanced proficiency in at least one modern programming language (e.g., Python, TypeScript, JavaScript, Go, Java). 

• Solid grounding in software architecture patterns, API design, data modeling, and full-stack or backend delivery. 

• Fluency in at least one major cloud environment (AWS, Azure, or GCP) and modern development practices (CI/CD, containers, version control workflows, testing). 

• Demonstrated experience applying AI tooling (e.g., Copilot, ChatGPT, Claude, vector search, prompt engineering, RAG patterns) in real delivery workflows. 

• Track record of shipping production software on tight timelines in a consultative or embedded setting. 

• Experience mentoring engineers and running effective code reviews. 

• Excellent communication skills, with the ability to translate between business stakeholders and engineers and to represent technical tradeoffs clearly. 

Preferred Education and Experience 

• Experience delivering AI, automation, or data-intensive applications in enterprise environments. 

• Experience working within large, decentralized organizations or multi-site enterprise environments.