Forward Deployed AI Engineer

Location: New York, NY

Compensation: $180-200K + Bonus

Responsibilities: Partner with stakeholders to identify operational friction, map complex systems, and translate business challenges into clear, measurable AI use cases. Define both long-term technical visions and immediate, high-impact MVPs to validate value quickly. Design, code, and deploy Generative AI solutions, AI agents, and automated workflows integrated with enterprise APIs, databases, and applications. Implement robust human-in-the-loop mechanisms, fallback controls, and enterprise-grade security and governance. Collaborate directly with software teams to scale prototypes into reliable, production-ready systems. Leverage coding agents and modern AI-driven development workflows (specification-driven development, context management) to accelerate delivery. Convert custom deployment patterns into reusable assets, reference architectures, and paved-road tools to inform the broader enterprise AI roadmap. Establish frameworks to measure model performance, UX, latency, cost, and business impact. Implement testing, monitoring, and controls to mitigate failure modes and comply with security, privacy, and responsible AI policies. Communicate complex technical concepts to non-technical stakeholders and executive leadership. Influence cross-functional decisions without direct authority and mentor partner teams through digital transformation.

Qualifications: 5+ years in software engineering, solution architecture, or technical consulting with a track record of shipping end-to-end products. Fluency in software engineering (backend, web, mobile, distributed systems, or API/workflow orchestration) with practical experience using coding agents in structured workflows. Demonstrated capability to analyze end-to-end systems, articulate technical tradeoffs, and influence cross-functional decisions without direct authority. Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).

Preferred: Experience building with LLMs, RAG, vector databases, tool calling, Model Context Protocols, or frameworks like LangGraph, LangChain, Temporal, or LlamaIndex. Hands-on experience with AI evaluation metrics, observability tools, and responsible AI safeguards (human-in-the-loop). Track record of driving technology adoption within financial services or other highly regulated industries.