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.
For applications and inquiries, contact: [email protected]