AI Engineering Director

About the role

We are seeking an AI Engineering Director to lead the AI strategy and development 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, with a player-coach track record.

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
• Define responsible AI practices appropriate for financial services - accuracy thresholds, auditability requirements, and appropriate human-in-the-loop controls
• Ensure AI outputs are explainable to both technical and non-technical users

Requirements

• Experience: 3+ years AI/ML engineering; 5+ years in a product development environment
• Technical Skills: Expert Python proficiency, deep production LLM experience, production experience with agentic frameworks, workflow orchestration experience, vector databases and embedding systems, active daily user of AI coding assistants
• Financial Services Background: Understanding of what accuracy and auditability mean in a compliance-sensitive context, MLOps and model deployment at scale, 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