Job Title: AI/ML Engineer Location: NYC, NY/Jersey City, NJ (Hybrid – 4 Day Onsite) Type: Fulltime • • We need A senior (10+ years) AL engineer with extensive experience working with deep expertise in Generative AI, neural networks, and transfer learning to support a strategic data analytics platform. This role will lead the design and development of cutting-edge ML models and architectures involving zero-shot/few-shot learning and retrieval-augmented generation (RAG). Ability to function as the primary contributor building Generative AI solutions alongside the team while guiding the rest of the team on best practices, code reviews etc. In depth knowledge of Machine Learning, NLP, Information extraction etc. • • Client is seeking an AI Engineer with deep expertise in Generative AI, neural networks, and transfer learning to support a strategic data analytics platform. This role will lead the design and development of cutting-edge ML models and architectures involving zero-shot/few-shot learning and retrieval-augmented generation (RAG). Hybrid Schedule: In-office 4 days per week Capital Markets Data AI and Research Technology (DART) team is looking to hire a seasoned “hands on” AI Scientist to lead our AI efforts in Quant and Tech Services. The role will be responsible for applying Generative AI techniques to build solutions for Capital markets use cases. What will you do? • Build agentic frameworks for capital markets use cases on summarization, conversational AI, information extraction etc. • Embed Generative AI tools and frameworks in current Capital Markets tools for personas like Research , Banking , Sales and Trading What do you need to succeed? Must-have • PhD or master’s degree in computer science, Machine Learning, Deep Learning or equivalent experience • 5+ years of experience building Deep Learning or Machine Learning models. • In depth knowledge of Machine Learning , deep learning and Generative AI. • Ability to function as the primary contributor building Generative AI solutions alongside the team while guiding the rest of the team on best practices, code reviews etc. • In depth knowledge of Machine Learning, NLP, Information extraction etc. • Knowledge or understanding of Embeddings, Re-rankers, agentic frameworks etc. • Knowledge or understanding of Inferencing, fine-tuning, model architectures etc. • In depth knowledge and experience building front to back AI Operations including experimentation, model evals and model monitoring • In depth knowledge of Algorithms and Data Structures • Knowledge of distributed computing