AI/ML Engineer (Fintech)

TL;DR

AI/ML Engineer (Fintech): Designing and architecting scalable end-to-end AI/ML solutions for compliance applications with an accent on GenAI-driven solutions, agentic frameworks, and RAG pipelines. Focus on automating compliance processes, managing risk at a massive data scale, and implementing cutting-edge LLM applications.

Location: Onsite in New York, NY, United States

Salary: USD 130,000 - 250,000

Company

A leading global financial institution dedicated to preventing and mitigating regulatory and reputational risks.

What you will do

• Architect and implement scalable AI/ML solutions for compliance, including GenAI agents, RAG pipelines, and embedding-based knowledge bases.
• Conduct experimentation with model fine-tuning, prompt engineering, and diverse algorithmic approaches to solve complex business challenges.
• Lead technical projects from inception to completion, overseeing the development of high-quality production code.
• Collaborate with compliance officers and legal counsel to translate regulatory requirements into technical specifications.
• Mentor junior engineers and establish best practices for AI/ML development, version control, and documentation.

Requirements

• Degree in Computer Science, Machine Learning, Mathematics, or a related field.
• 7+ years of AI/ML industry experience (or 4+ years for PhD), specifically focusing on Language Models.
• Proficiency in Python and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, and scikit-learn.
• Expertise in GenAI techniques, including RAG, model fine-tuning, AI agents, and evaluation methods.
• Experience with vector databases and MLOps practices (Docker, Kubernetes, CI/CD).
• Must be based in or able to work from the New York office.

Nice to have

• Experience with Agentic Frameworks such as Langchain or AutoGen.
• Knowledge of performance optimization for real-time inference (quantization, pruning, knowledge distillation).
• Familiarity with financial regulations and compliance requirements.
• Experience with model interpretability and distributed systems architecture.

Culture & Benefits

• Access to petabyte-scale structured and unstructured data for model training and evaluation.
• Opportunity to work with state-of-the-art LLMs and advanced agentic frameworks.
• Collaborative environment working alongside a global team of experienced scientists and engineers.
• Ability to make a tangible impact on the risk management and reputation of a major global firm.