AI Builder​/Forward Deployed Engineer

Position Overview

Our client is on a journey to become an AI‑first organization. They believe artificial intelligence will reshape the workplace by automating repetitive tasks and enabling teams to focus on higher‑value, strategic work.

AI Builder / Forward Deployed Engineer will work closely with business teams across investment, finance, compliance and operations to identify high‑value opportunities and build practical AI‑enabled solutions. As part of a small and agile AI and Data team, the role combines technical development, experimentation and cross‑functional collaboration to rapidly prototype, deploy and scale solutions that improve how work is done across the firm.

This is fundamentally a full‑stack engineering role. The majority of the work involves building the data pipelines, system integrations, and foundational infrastructure that make AI solutions possible -- not just the AI layer itself. Strong engineering fundamentals matter as much as AI fluency here.

• Partner with business units across investment, finance, compliance and operations to understand operational problems, scope technical solutions, and rapidly prototype and iterate based on real‑world feedback.
• Design and build data pipelines that move, transform, and prepare data from internal systems for use in AI and analytics workflows.
• Own end‑to‑end integrations between internal platforms and third‑party APIs, including authentication, error handling, and ongoing maintenance.
• Contribute to foundational infrastructure decisions -- data storage, service architecture, deployment patterns -- as the firm’s data and AI platform matures.
• Build and deploy production‑ready AI applications, including RAG pipelines, agentic workflows, and LLM integrations, with a pragmatic eye toward what actually works at scale.
• Ensure solutions meet production standards for security, access controls, responsible AI usage, and ongoing maintainability.

• Bachelor’s degree in Computer Science, Engineering or a related field, or equivalent practical experience.
• 2–5 years of experience in software engineering roles, with demonstrated ownership of production systems end‑to‑end.
• Solid full‑stack engineering fundamentals -- backend services, relational and document databases, REST and webhook integrations, async patterns, and cloud deployment.
• Hands‑on experience building and maintaining data pipelines and system integrations in production environments, not just application‑layer prototypes.
• Practical experience with LLM‑based systems in production -- including RAG pipelines, agentic workflows, and LLM integrations -- and sound judgment on when AI adds value over conventional approaches.
• Curious, self‑directed, and biased toward action -- able to work directly with non‑technical stakeholders, move fast in ambiguous situations, and drive problems to resolution without heavy oversight.
• Preferred: experience in financial services or alternative asset management, familiarity with structured financial data and compliance constraints.