Machine Learning Engineer - The Connors Group : Job Details

Machine Learning Engineer – Artificial Intelligence Team (TRAIT)

Hybrid – 3 Days Onsite / 2 Days Remote

Our client's Artificial Intelligence Team (TRAIT) is seeking a talented Machine Learning Engineer to deliver key ML capabilities to a global user base. This individual will be responsible for developing, scaling, and deploying state-of-the-art machine learning models and applications in a hybrid work environment.

Key Responsibilities:

• Design and build end-to-end machine learning pipelines: data collection, normalization, standardization, model selection, hyperparameter tuning, and continuous learning systems.
• Research and apply the latest advancements in LLMs and Generative AI, focusing on model safety, interpretability, and innovative use cases.
• Develop and deploy ML-powered applications, both in the cloud and on-premise.
• Maintain and update data pipelines to support existing applications with fresh and relevant data.
• Manage the ML modeling ecosystem, including scalability, performance, and versioning.
• Identify and integrate orthogonal data sources to enhance model performance and increase alpha.
• Stay current with advancements in ML technologies and incorporate best practices into the team's architecture and workflows.
• Collaborate with (re)insurance domain experts to enhance predictive modeling within various lines of business.

Qualifications:

• Demonstrated success in building, scaling, and productizing machine learning models across multiple use cases.
• Deep understanding of the machine learning lifecycle: from concept and research to deployment and support.
• Expertise with Large Language Models (LLMs): fine-tuning, reinforcement learning with human feedback (RLHF), distillation, and performance optimization.
• Strong experience with high-dimensional, sparse, tabular, and time series data.
• Proficient in Python and the broader ML ecosystem (e.g., PyTorch, TensorFlow, CUDA).
• Familiarity with ensemble methods including boosting, bagging, stacking, and meta-learners.
• Solid understanding of ML optimization techniques.

Work Environment:

This role offers a hybrid schedule—3 days per week in-office and 2 days remote. Work schedules may vary depending on the office location, business needs, and market trends, but a flexible and agile environment is encouraged.