Forward Deployed Engineer

Job description

Location: Hybrid (NYC) - Remote (United States) Compensation: $149K - $185K Our client is a high-growth technology organization building an advanced AI platform designed for enterprise operations and value creation in an AI-native world. Founded by experienced leaders in applied AI and mission-critical infrastructure, the organization leverages a proven track record of developing leading risk management and financial infrastructure that has processed trillions in volume. Backed by top-tier venture capital firms with over $65 million in funding, the company maintains an agile, elite team focused on solving technically ambitious problems. Operating with a culture centered on high-agency, talent-dense individual contributors, our client emphasizes high autonomy, meaningful ownership, and strong judgment over hierarchy. They are seeking a Forward Deployed Engineer to own the technical customer journey and turn complex enterprise challenges into high-value, production-grade AI solutions. In this high-impact role, the successful candidate will bridge technical strategy and customer execution—partnering closely with product, sales, and engineering leaders to guide strategic clients from technical discovery through implementation, production rollout, adoption, and scaling. Key Responsibilities End-to-End AI Deployments: Own strategic client engagements throughout the lifecycle, including technical discovery, scoping, architecture design, hands-on implementation, evaluation, production rollout, and long-term adoption. Pre-Sales & Solution Architecture: Partner with the commercial team during pre-sales phases to lead technical discovery, design tailored AI solutions, and perform validation for high-value customer opportunities. Post-Sales Technical Execution: Drive post-sales success by building and integrating production AI systems that transition clients from initial concept into full production environments. Production AI Systems Engineering: Develop scalable AI applications utilizing modern large language models (LLMs), model APIs, tool calling, retrieval-augmented generation (RAG), context management, structured outputs, and agentic workflows. Product Optimization & Feedback Loops: Translate recurring customer insights into product enhancements—identifying cross-deployment patterns and developing reusable components, playbooks, tooling, and core product features…