Hybrid: Gen AI Lead - Financials

To summarize he needs all the below :
1- Someone with a financial domain expertise with a huge preference to someone who has a private alternatives  experience, who can interface with the business and understand requirements.
2- Someone who has AI architecture design and tools hands on experience .

Must have strong Financial Services experience  and Private Alternatives experience must be strong

Key Responsibilities

• Lead the end-to-end development and deployment of Generative and Agentic AI solutions for Private Alternatives investment processes.
• Work closely with a multidisciplinary AI pod squad, including data engineers, front end developers, and investment domain experts.
• Collaborate closely with investment professionals, technology partners, and external vendors to identify and prioritize high-impact use cases.
• Design and implement AI-driven tools for deal research, pipeline management, diligence automation, portfolio monitoring, and disposition analysis.
• Ensure AI solutions align with regulatory requirements, data privacy standards, and industry best practices.
• Establish and monitor key performance indicators (KPIs) to measure efficiency gains, automation impact, and investment outcomes.
• Drive continuous improvement and scalability of AI models and agentic workflows across multiple funds and investment strategies.
• Stay abreast of emerging AI technologies, frameworks, and market trends relevant to Private Equity and Private Credit.
• Champion a culture of experimentation, rapid prototyping, and knowledge sharing within the AI pod and across the organization.

Required Qualifications

• Bachelor’s in computer science, Data Science, Artificial Intelligence, or a related quantitative discipline.
• Minimum 10 years of hands-on experience in building Enterprise Scale front to back applications with strong recent exposure to AI/ML development, with at least 3 years in a leadership role overseeing cross-functional teams specially in GenAI or Agentic AI space.
• Demonstrated expertise in GenAI (large language models, generative frameworks) and Agentic AI (autonomous agents, workflow orchestration).
• Strong understanding of Private Alternatives, particularly Private Equity and Private Credit investment processes.
• Proven track record in designing and implementing AI solutions for financial services, investment management, or asset management domains.
• Familiarity with compliance, data governance, and security considerations in regulated financial environments.

Technical Skills

• Core skill required great leadership, communication and collaborations skill along with AI acumen.
• Advanced proficiency in Python, Java, or similar programming languages for AI development utilizing OpenAI or other leading LLM Model providers.
• Exposure to Microsoft Copilot Studio and Microsoft Power Apps in building AI Enabled low-code/no-code solutions.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Keras, and agentic orchestration platforms (e.g., LangChain, AutoGPT).
• Expertise in data engineering, feature extraction, and model deployment (cloud and on-premise).
• Experience integrating AI tools with investment lifecycle management systems (e.g., Salesforce CRM, DealPath, DealCloud, eFront, Investran, Snowflake).
• Knowledge of NLP, generative modeling, reinforcement learning, and agent-based simulation.
• Ability to troubleshoot, optimize, and scale AI models in production environments.

Leadership and Soft Skills

• Proven ability to lead and inspire high-performing technical teams.
• Exceptional stakeholder management and cross-functional collaboration skills.
• Strong written and verbal communication skills, with the ability to translate complex technical concepts for non-technical audiences.
• Strategic thinker with a bias for action and results-oriented execution.
• Commitment to fostering a culture of inclusion, innovation, and continuous learning.

Preferred Experience

• Prior work in Private Alternatives (Private Equity, Private Credit, or related asset classes).
• Experience automating investment lifecycle stages through intelligent workflows and AI-driven tools.
• Track record of delivering measurable process improvements in investment research, deal execution, or portfolio management.

Success Metrics

• Reduction in manual effort and process turnaround times across investment lifecycle stages.
• Increase in actionable insights, data accuracy, and automation adoption within Private Alternatives teams.
• Achievement of targeted KPIs related to deal throughput, diligence efficiency, and portfolio monitoring effectiveness.
• Positive feedback from investment professionals and stakeholders regarding AI solution usability and impact.