Applied AI Engineering Director for Banking Technology

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Job Overview

Applied AI Engineering Director for Banking Technology (Hybrid)

Role Summary

We are looking for a highly motivated, hands-on Applied AI Engineering Senior Lead for Banking Technology to lead the design, development, and deployment of cutting-edge, AI-first solutions for our Banking division, covering Investment, Corporate, and Commercial Banking. This pivotal role will bridge the front office with advanced technology, championing an AI-first mindset to drive intelligent automation and data-driven decision-making directly into the heart of dealmaking processes. The ideal candidate will combine deep, hands-on AI engineering expertise with a strong understanding of the investment banking ecosystem, business workflows, and the secure, enterprise-scale deployment of Agentic AI solutions.

Key Responsibilities

• Strategic AI Leadership: Partner with senior bankers and business leads to identify high-impact AI opportunities across deal origination, client intelligence, market analysis, and pitch automation. Develop and execute a comprehensive AI engineering roadmap aligned with Banking tech strategy and enterprise architecture.
• AI Engineering & Architecture: Lead the design and development of scalable, robust AI systems, including advanced Large Language Models (LLMs), Natural Language Processing (NLP), knowledge graphs, and machine learning pipelines. Architect and implement secure, compliant Agentic AI solutions that seamlessly integrate with market data, CRM, internal knowledge bases, and document repositories.
• Data Strategy: Drive the strategic integration of both structured (e.g., financial data, CRM) and unstructured (e.g., filings, call transcripts, news) data, enabling advanced insights and sophisticated AI model training. Oversee data engineering and ML feature pipelines in close collaboration with data teams.
• Productization & Delivery: Convert innovative proofs-of-concept into scalable, enterprise-grade tools and Agentic AI solutions. Embed AI capabilities directly into banker workflows via intuitive agents, dynamic dashboards, and smart document assistants, ensuring real-world impact.
• Governance & Compliance: Establish and enforce rigorous standards to ensure all AI systems meet internal requirements for explainability, fairness, and compliance with regulatory obligations. Collaborate proactively with risk, legal, and compliance teams on AI model governance.
• Team Building & Leadership: Recruit, mentor, and lead a high-performing team of AI engineers, ML specialists, and applied data scientists. Foster a culture of continuous innovation, delivery excellence, and strong business alignment.

Qualifications

Must-Have Skills & Experience

• 15+ years of industry experience, primarily in data science and AI engineering, with a minimum of 4 years in a leadership role, preferably within financial services or other highly regulated enterprise environments.
• Demonstrated success in building and deploying impactful AI applications, particularly in investment banking, asset management, or capital markets domains.
• Deep, hands-on technical expertise in machine learning (ML), natural language processing (NLP), large language models (LLMs), retrieval-augmented generation (RAG), and modern MLOps practices.
• Proven experience in building and deploying Agentic AI solutions using frameworks like Google ADK, Langraph, etc, showcasing a strong AI-first approach to problem-solving.
• Strong experience working with both structured financial datasets and diverse unstructured data sources (e.g., regulatory filings, call transcripts, market research).
• Familiarity with front-office workflows across ECM, DCM, M&A, and investment research.
• Extensive experience deploying AI solutions in secure, high-compliance environments (on-premise, hybrid cloud, or private cloud).
• Exceptional communication, presentation, and stakeholder management skills, with a track record of influencing senior bankers and C-level executives.

Preferred

• Experience with knowledge graphs and graph-based search technologies.
• Familiarity with industry-standard financial data tools such…