Sr.Machine Learning Engineer

Key Skills

Amazon EMR, AWS, AWS Glue, AWS Sagemaker, Azure, BERT, Cloud computing, Computer Vision (CV), Deep Learning, Docker, HuggingFace, JIRA, Julia, Kubernetes-K8s, Lambda, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), Neural Networks, NumPy, Pandas, Python Programming, PyTorch, Scikit-learn, SQL, TensorFlow, Transformer

Job Description

As a Senior Machine Learning Engineer at JPMorgan Chase within Wealth Management Technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. You will get an opportunity to stay at the forefront of technology, contributing to innovative solutions that make tangible impact in personal and professional growth. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. You will use data to understand the product and business ecosystem, quantify new opportunities, identify upcoming challenges, and share product development to bring value to clients and employees. You will help develop strategy and influence leaders using clear insights and recommendations.

Key responsibilities include

• Model Development and Deployment: Design, Develop, and deploy advanced machine learning models, with expertise in LLM, BERT, Neural Networks, classification, and deep learning.
• Data Analytics and Visualization: Use data to derive patterns and insights to support data-driven business decisions.
• Software and Model Development Life Cycle (S[M]DLC): Implement best practice in SDLC and MDLC to ensure efficient and robust model development, testing and deployment.
• Continuous Integration and Continuous Deployment (CI/CD): Implement CI/CD pipelines to automate model deployment and monitoring.
• Teamwork and Leadership: Collaborate with cross functional teams, including data scientists, software engineers, and business stakeholders to identify opportunities and deliver data-driven solutions that drive business growth and enhance client satisfaction.

Required qualifications, capabilities, and skills:

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, related technical field or equivalent technical experience.
• A minimum of 8 years of experience (minimum of 6 years with a Ph.D.) in one or more of the following: deploying classification, Neural Networks, BERT, LLM, Ranking, Recommendations, Personalization or other advanced machine learning models in production environment.
• Experience with building and deploying ML models on AWS using tools like Sage maker, EKS, Lambda, Glue, etc.
• Proficiency in SQL, Python, AWS, Kubernetes, Docker, Jules, JIRA
• Strong knowledge of Natural Language Processing (NLP), Computer Vision, and Deep learning

Preferred qualifications, capabilities, and skills:

• Ph.D. in a quantitative field.
• Strong research record demonstrated through publications.
• AWS or Azure certified.
• Knowledge of designing Machine Learning Architecture framework
• Extensive experience with Machine Learning and Deep Learning toolkits (e.g.: Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)