AI GenAI Lead

Role: Gen AI Lead Location: NYC, NY Duration: 12 Months 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 . 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.