A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
Are you ready to make a real impact with cutting-edge AI technology? Join a high-performing team where you'll help build next-generation AI applications and platforms for the Private Bank. You'll work with modern cloud infrastructure, robust engineering practices, and collaborate with talented colleagues across the globe. This is your opportunity to contribute hands-on to meaningful projects and accelerate your career in AI. As a Machine Learning Engineer Associate Intern in the Asset and Wealth Management AI Engineering team, you will develop and deploy advanced machine learning and generative AI solutions. You will work closely with technology and product partners to address high-impact business challenges, leveraging state-of-the-art models and engineering practices in a collaborative, fast-paced environment.
Job responsibilities:
Develop solutions using large language models for content extraction, search, Q&A, reasoning, and recommendations. Build comprehensive evaluations and tests to ensure model performance and reliability. Collaborate with technology and product partners in a fast-paced, distributed environment. Design and implement scalable data pipelines and deploy model inference services in the cloud. Experiment, develop, and productionize high-quality machine learning models and services. Stay current with the latest research and apply emerging techniques to solve business problems. Required qualifications, capabilities, and skills Currently pursuing an advanced degree (MS or PhD) in Computer Science, Data Science, or Machine Learning. Hands-on experience with natural language processing and large language models. Strong programming skills in Python. Familiarity with machine learning libraries. Solid understanding of data structures, algorithms, machine learning, data mining, information retrieval, and statistics. Excellent communication skills, with the ability to engage senior technical and business stakeholders. Active participation in university or open-source AI/ML projects. Preferred qualifications, capabilities, and skills Exposure to cloud computing platforms such as Amazon Web Services (AWS), Azure, or Kubernetes. Project or work experience in Natural Language Processing, Reinforcement Learning, or Ranking and Recommendation. Experience with other high-performance languages is a plus.
