Machine Learning Engineer
100% Remote within the US / Must have background in financial industry. Responsibilities As a Machine Learning Engineer, you will be a crucial part of our tech team, focused on the innovative application of machine learning techniques across various domains.
Your key responsibilities will include : Building and enhancing machine learning models through all phases of development, including design, training, validation, and implementation.
Unlocking insights by analyzing large scales of complex numerical and textual data and identifying trends. Collaborating with a cross-functional team, including data engineers, data scientists, and data visualization experts, to deliver impactful projects.
Researching and evaluating emerging technologies in the field. Developing data science solutions leveraging modern tools and cloud computing infrastructure.
Fulfilling additional duties as assigned. Qualifications The ideal candidate will possess : A Bachelor’s degree in Computer Science, Mathematics, Physics, Statistics, or a related field.
Demonstrated experience with model design, training, validation, and monitoring. An excellent understanding of machine learning, statistical modeling, and algorithms, including their benefits and drawbacks.
Advanced proficiency with Python, Jupyter Notebook / Lab, Visual Studio Code, and other languages suitable for large-scale data analysis.
Experience with cloud computing infrastructure. Advanced SQL skills. Familiarity with data visualization concepts and tools.
The ability to translate complex business problems into technical solutions. Strong capability to work both independently and as part of a team.
Exceptional verbal, written, interpersonal, and presentation skills to communicate technical and non-technical information to all levels of management.
Desired Skills Preferably, candidates will also have : An advanced degree in Computer Science, Mathematics, Physics, Statistics, or a related field.
Experience with Natural Language Processing (NLP). Proficiency with deep learning frameworks and infrastructure, such as TensorFlow or PyTorch.
An eagerness to learn and apply techniques in Large Language Models (LLMs) and Generative AI. Expertise in AI model optimization on GPU architecture, including knowledge of C++ and CUDA.
The willingness to research, develop, implement, and fine-tune LLMs tailored to specific domain knowledge and use cases.
Knowledge of Machine Learning Ops (MLOps) and CI / CD tools for automating the build, test, and deployment of models in production environments. Powered by JazzHR
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