Head of Applied AI Engineering – Investment Banking

Location: Pune, Maharashtra, India

Employment Type: Regular

The Applications Development Senior Group Manager is a senior management level position responsible for accomplishing results through the management of a team or department in an effort to establish and implement new or revised application systems and programs in coordination with the Technology Team. The overall objective of this role is to drive applications systems analysis and programming activities.

Role Summary
We are seeking a visionary and hands-on Head of Applied AI Engineering to lead the design, development, and deployment of AI-driven solutions for our Banking division, covering Investment (ECM, DCM, M&A), Corporate and Commercial Banking. This role will bridge the front office and advanced technology, bringing intelligent automation and data-driven decision-making into the heart of dealmaking.

The ideal candidate will combine deep AI engineering expertise with a strong understanding of the investment banking ecosystem, business workflows, and secure enterprise-scale deployment.

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 the AI engineering roadmap aligned to Banking tech strategy and enterprise architecture.
AI Engineering & Architecture
Lead the design and development of scalable AI systems using LLMs, NLP, knowledge graphs, and machine learning pipelines.
Architect secure, compliant AI solutions that integrate with market data, CRM, internal knowledge bases, and document repositories.
Data Strategy
Drive integration of structured (e.g., financial data, CRM) and unstructured (e.g., filings, call transcripts, news) data to enable advanced insights.
Oversee data engineering and ML feature pipelines in collaboration with data teams.
Productization & Delivery
Convert proofs-of-concept into scalable, enterprise-grade tools.
Embed AI solutions into banker workflows via co-pilots, dashboards, and smart document assistants.
Governance & Compliance
Ensure all AI systems meet internal standards for explainability, fairness, and compliance with regulatory obligations.
Collaborate with risk, legal, and compliance teams on AI model governance.
Team Building & Leadership
Build and lead a high-performing team of AI engineers, ML specialists, and applied data scientists.
Foster a culture of innovation, delivery excellence, and business alignment.

Qualifications:

• Must-Have Skills & Experience
  10+ years in data science / AI engineering, with 4+ years leading teams in financial services or enterprise environments.
  Demonstrated success building AI applications in investment banking, asset management, or capital markets domains.
  Deep expertise in ML, NLP, LLMs, retrieval-augmented generation (RAG), embeddings, and modern MLOps practices.
  Strong experience working with both structured financial datasets and unstructured data sources (e.g., filings, call transcripts, research).
  Familiarity with front-office workflows in ECM, DCM, M&A, and investment research.
  Experience deploying AI solutions in secure, high-compliance environments (on-premise, hybrid cloud, or private cloud).
  Strong communication and stakeholder management skills, especially with senior bankers and C-level executives.

What Success Looks Like
AI tools embedded into daily workflows of bankers and analysts.
Reduction in manual effort across client targeting, pitch preparation, and market monitoring.
Data assets and ML models fully aligned with enterprise governance and architecture.
Scalable AI platform that evolves with the pace of the business and innovation.

Why Join Us?
Shape the future of investment banking with cutting-edge AI.
Work at the intersection of technology, capital markets, and innovation.
High visibility and impact across the front office and C-suite.
Access to world-class data, partners, and AI infrastructure.

Education:

• Bachelor’s degree/University degree or equivalent experience
• Master’s degree preferred

Preferred
Experience with knowledge graphs and graph-based search.
Familiarity with tools like Bloomberg, Refinitiv, Capital IQ, FactSet, PitchBook.
Prior work on AI co-pilots, document summarization tools, or automated pitch generation.
Exposure to enterprise CRM systems and client intelligence platforms.
Advanced degree in Computer Science, AI, Applied Mathematics, or related quantitative field.

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