THIS POSITION IS VERY HANDS ON AND CANDIDATES WILL DO AT LEAST 80% CODING.

Seeking an AI Scientist 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 3 days per week

Key Responsibilities:

• Research, develop, and deploy Generative AI models for enterprise-scale use cases in capital markets.
• Design and optimize architectures involving RAG, fine-tuning, and multi-model orchestration.
• Apply zero-shot and few-shot learning using foundation models and prompt engineering strategies.
• Collaborate with engineering, data science, and product teams to productionize AI capabilities.
• Monitor model performance, conduct A/B testing, and ensure model robustness and compliance.

Must-Have:

• 3+ years of experience in applied AI/ML, including recent hands-on work with LLMs, transfer learning, and Generative AI architectures.
• Strong understanding of transformer-based models and deep learning frameworks.
• Experience with RAG systems, model fine-tuning, and large-scale deployment.
• Proficiency in Python, PyTorch, Hugging Face Transformers, and modern MLOps practices.
• Familiarity with scalable system design for AI/ML workflows.
• Experience in financial services, capital markets, or enterprise data platforms.

Job Types: Full-time, Permanent, Fixed term contract
Contract length: 12 months
Pay: $91,731.01-$150,000.00 per year

Application question(s):

• What’s your visa status and current location?

Experience:

• AI/ML: 4 years (required)
• LLMs, transfer learning, and Generative AI architectures.: 4 years (required)
• transformer-based models : 3 years (required)
• Retrieval-augmented generation (RAG): 2 years (required)
• Capital Markets/Banking : 2 years (preferred)

• Research, develop, and deploy Generative AI models for enterprise-scale use cases in capital markets.
• Design and optimize architectures involving RAG, fine-tuning, and multi-model orchestration.
• Apply zero-shot and few-shot learning using foundation models and prompt engineering strategies.
• Collaborate with engineering, data science, and product teams to productionize AI capabilities.
• Monitor model performance, conduct A/B testing, and ensure model robustness and compliance