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JPMC Candidate Experience page
Applied AI ML Engineer-Vice President
JPMC Candidate Experience page
New York•On-site•2-5 Yrs•3 hours ago
Job description
Overview
The Corporate & Investment Bank is a global leader across investment banking, wholesale payments, markets and securities services.
Responsibilities
- Develop and implement machine learning models and algorithms to solve complex operational challenges.
- Design and deploy generative AI applications to automate and optimize business processes.
- Collaborate with stakeholders to understand business needs and translate them into technical solutions.
- Analyze large datasets to extract actionable insights and drive data-driven decision-making.
- Ensure the scalability and reliability of AI/ML solutions in a production environment.
- Stay up-to-date with the latest advancements in AI/ML technologies and integrate them into our operations.
- Mentor and guide junior team members in AI/ML best practices and methodologies.
Qualifications
- Ph.D. with 2+ years experience or Masters with 4+ years experience in Computer Science, Data Science, Machine Learning, or a related field.
- Familiarity with MLOps practices, encompassing the full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models.
- Expertise in machine learning frameworks such as TensorFlow, PyTorch, Pytorch lightening, or Scikit-learn.
- Proficiency in programming languages such as Python
- Proficiency in writing comprehensive test cases, with a strong emphasis on using testing frameworks such as pytest to ensure code quality and reliability.
- Solid understanding of data preprocessing, feature engineering, and model evaluation techniques.
- Familiarity with cloud platforms (AWS) and containerization technologies (Docker, Kubernetes, Amazon EKS).
- Excellent problem-solving skills and the ability to work independently and collaboratively.
- Strong communication skills to effectively convey complex technical concepts to non-technical stakeholders.
- Familiarity with distributed computing systems, frameworks and techniques like data sharding and DDP training