Forward Deployed Engineer

This is a role posted by Reval Recruiting on behalf of a client

Forward Deployed Engineer

Location: Remote (with significant travel; frequent on-site time with portfolio companies)

This role is posted on behalf of the following client: a growth-focused private equity investment firm with an active operating team that partners closely with portfolio company leadership. The firm supports multiple B2B software businesses and invests in hands-on product and engineering transformation to accelerate growth and modernization across the portfolio.

Role Overview

A private equity operating team is seeking a Forward Deployed Engineer who builds—not advises. This is a hands-on, embedded role working inside multiple portfolio companies at a time, partnering with CPOs and CTOs to identify AI-native product opportunities, refactor legacy systems into AI-first architectures, and ship production software that customers use. Engagements typically run ~6 months per company, with meaningful time on-site, commit access to production repos, and a bias toward shipping in weeks and measuring success by business impact.

What You'll Do

• Embed with portfolio product and engineering teams to build end-to-end features across frontend, backend, data, and infrastructure—shipping production-grade software with testing, monitoring, and documentation.
• Modernize legacy products into AI-first architectures by prototyping quickly (days), validating the highest-impact use cases, and scaling prototypes into maintainable systems (e.g., assistants, workflow automation, predictive/intelligent product features).
• Shape and accelerate AI adoption across the portfolio by scouting and benchmarking new AI tools/frameworks, curating reusable components (including MCP servers/tools), and training teams via pairing, code reviews, playbooks, and workshops.

Who You Are

• A true full-stack builder who owns outcomes end-to-end (not partial handoffs), with strong product judgment and the ability to partner directly with CPO/CTO stakeholders to translate opportunities into roadmaps and shipped capabilities.
• AI-native in day-to-day development: experienced with AI-assisted IDE workflows (e.g., Cursor, Claude Code, Copilot), comfortable selecting and integrating LLM APIs, and fluent in building practical agent/RAG systems (vector DBs, evaluation, tooling/extensibility such as MCP).
• Comfortable operating across multiple companies and contexts with high autonomy; experienced modernizing legacy systems incrementally without breaking production; willing to travel significantly and spend meaningful time on-site with portfolio companies.