This position requires an A.I. Engineer to build and enhance machine learning models through all phases of development, including design, training, validation, and implementation. The role involves analyzing large-scale numerical and textual data to identify trends, reviewing A.I. system architectures to evaluate risks, and performing A.I. risk assessments. The engineer will partner with cross-functional teams and research emerging technologies to develop data science solutions.
Key Responsibilities
Build and enhance machine learning models through all phases of development including design, training, validation, and implementation.
Unlock insights by analyzing large scale of complex numerical and textual data and identifying trends.
Review A.I. system architectures and data flows to identify exposure to risks and evaluate identified A.I. safety and security risks.
Perform A.I. risk assessments across the full lifecycle, including data security, model development, deployment, and ongoing monitoring.
Partner with a cross-functional team of data engineers, data scientists, and data visualization to deliver projects.
Research and evaluate emerging technologies.
Develop data science solutions based on tools and cloud computing infrastructure.
Required Qualifications
Education: An advanced degree in computer science, mathematics, physics, or statistics is required.
Experience: Candidates should possess experience in model design, training, validation, and monitoring. A strong understanding of machine learning, statistical modeling, and algorithms is necessary.
Technical Skills
Experience with cloud computing infrastructure.
Experience with Computer Vision, image processing, and video analytics.
Experience with Natural Language Processing/Natural Language Understanding.
Experience with deep learning frameworks like TensorFlow or PyTorch.
Experience with or willingness to learn Agentic A.I. frameworks and Large Language Models (LLMs).
Experience with or willingness to research, develop, implement, and fine-tune LLMs.
Ability to work individually and as part of a team.
Advanced verbal, written, interpersonal, and presentation skills.
Preferred Qualifications
Experience reviewing A.I. system architectures and data flows to identify risks.
Experience performing A.I. risk assessments across the full lifecycle.
A.I. Model Optimization on GPU architecture, leveraging C++ and CUDA.
Knowledge of Machine Learning Ops and CI/CD tools.
Knowledge of the principles of A.I. Security and Safety.
Experience with Advanced Reinforcement Learning Paradigms.
Experience with Speech Recognition.
Experience with Microsoft Azure services.