AI Engineering Director

About the role

We are seeking an AI Engineering Director to lead the AI strategy and architecture for our AI-native data integration and transformation platform. The ideal candidate will have 3+ years of AI/ML engineering experience and 5+ years in a product development environment. They should have a player-coach track record, deep technical skills, and a strong understanding of responsible AI practices in financial services.

Responsibilities

• Execute the AI strategy - LLM selection and management, agentic application architecture, RAG system design, prompt engineering standards, and evaluation frameworks
• Help design and implement the core AI capabilities: transformation generation from natural language, integration configuration assistance, data quality detection, and intelligent validation
• Determine where AI adds genuine value vs. where deterministic logic is more appropriate - this judgment is critical for a financial services product
• Partner with the Head of Engineering and Head of Design on cross-functional AI feature development
• Remain deeply technical - architect and implement core AI features
• Build and evolve the transformation generation engine, integration suggestion system, and intelligent validation layer
• Design the AI pipeline architecture that operates reliably inside Agno-orchestrated workflows
• Establish evaluation, monitoring, and continuous improvement practices for production AI systems
• Build frameworks to measure AI output quality - accuracy, consistency, and user acceptance rates
• Implement production monitoring and model drift detection

Requirements

• Experience 3+ years AI/ML engineering; 5+ years in a product development environment
• Player-coach track record - has led teams while remaining deeply hands-on
• Technical Skills: Expert Python proficiency, deep production LLM experience, production experience with agentic frameworks, workflow orchestration experience, Azure (primary) or AWS, vector databases and embedding systems, active daily user of AI coding assistants
• Financial services, fintech, or regulated industry background - understanding of what accuracy and auditability mean in a compliance-sensitive context
• MLOps and model deployment at scale experience
• Experience fine-tuning open-source LLMs for domain-specific tasks
• Publications, conference talks, or open-source AI contributions
• Experience building AI features for data tools, analytics platforms, or enterprise SaaS products