Applied AIML Lead Research Engineer

Overview

In this VP-level role, you lead applied AI research to build production-ready capabilities for the IPB AIML team. You evaluate open-source LLMs, fine-tune models, and explore efficient inference and multimodal methods, translating research into shipped features. You collaborate with engineers to bridge prototyping and production, while advancing the team’s capabilities and demonstrating thought leadership. This is an opportunity to shape AI-powered experiences in a high-stakes financial context, leveraging cutting-edge techniques to drive impact.

Responsibilities

• Lead applied research on open-source LLM evaluation and deployment focusing on cost, data residency, and sovereignty options
• Execute fine-tuning for tone, lexicon, and domain-specific tasks to improve prompt reliability
• Investigate production inference efficiency, tokenomics, model routing, and optimization
• Prototype speech, voice, and multimodal capabilities to inform future user experiences
• Design rigorous evaluation methodologies and benchmarks for measurable, reproducible research
• Collaborate with AI and platform engineers to productionize research outputs tied to real platforms or product needs
• Publish internal findings and contribute to knowledge-sharing and recruitment/leadership
• Champion diversity, inclusion, and respectful culture within the team

Key requirements

• Formal AI/ML training or certification with applied experience
• Advanced proficiency in Python and the ML/DL stack (e.g., PyTorch, Hugging Face)
• Hands-on experience evaluating, fine-tuning, and deploying Large Language Models
• Strong experimental design, evaluation, and benchmarking of ML systems
• Ability to bridge research and engineering to produce production-ready prototypes
• Awareness of inference cost, performance, and optimization techniques
• Strong communication skills translating research into business/engineering terms
• Published in top-tier AIML venue or equivalent open-source/applied contributions in LLMs, fine-tuning, or efficient inference

• Strong communication and storytelling with technical and non-technical audiences
• cross-functional collaboration
• recruiting and thought-leadership mindset
• Large Language Models (evaluation, fine-tuning, deployment)
• Python
• PyTorch

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