Applied Ai/ml - Vice President

JPMorgan Chase JPMorgan Chase · Banking · Chicago, IL +1 · Commercial & Investment Bank

Vice President in the CIB Technology team at JPMorgan Chase, focusing on analyzing business problems, experimenting with state-of-the-art models, and developing machine learning and deep learning solutions. The role involves leading AI/ML initiatives, defining roadmaps, developing models for NLP, personalization, and recommendation systems, and operationalizing end-to-end ML pipelines for use cases like Document Q&A, Search, and Information Retrieval. It also includes building batch and real-time prediction pipelines, collaborating with various teams for production deployment, and working with LLMs and Gen AI solutions.

What you'd actually do

  1. Lead programs and provide directions to successfully implement the large AI/ML initiatives
  2. Assist product leadership in defining the problem statements, execution roadmap
  3. Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as NLP, personalization, or recommendation systems.
  4. Collaborate with business, operations, and other technology colleagues to understand AI needs and devise possible solutions.
  5. Develop end-to-end ML/AutoML/AutoNLP pipelines and operationalize the end-to-end orchestration of the ML models to support the various use cases like Document Q&A, Search, Information Retrieval, classification, personalization, etc.

Skills

Required

  • BS or MS or PhD in Computer Science or Data Science or Statistics or Mathematical sciences or Machine Learning
  • Strong background in Mathematics and Statistics
  • 7+ years’ experience in applying data science, ML techniques to solve business problems
  • one of the programming languages like Python, Java, C/C++
  • Experience with LLMs and Prompt Engineering techniques
  • 1+ year of experience working with Gen AI solutions / LLMs such as GPT, Claude, Llama etc.
  • Solid background in NLP, Generative AI
  • hands-on experience and solid understanding of Machine Learning and Deep Learning methods
  • familiar with large language models
  • Extensive experience with Machine Learning and Deep Learning toolkits (e.g.: Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
  • Ability to design experiments and training frameworks
  • outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
  • Experience with Big Data and scalable model training
  • solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
  • Experience with building and deploying ML models on AWS esp. using AWS tools like Sagemaker, EC2, Glue, etc.
  • good understanding about the Active Learning, Agent/Multi Agent Learning, Learning from Supervision/Feedback, etc.
  • Scientific thinking
  • Ability to invent and to work both independently and in highly collaborative team environments
  • Ability to work on tasks and projects through to completion with limited supervision
  • Excellent communication skills and team player

Nice to have

  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journals
  • Experience with A/B experimentation and data/metric-driven product development
  • Ability to develop and debug production-quality code
  • familiarity with continuous integration models and unit test development

What the JD emphasized

  • 7+ years’ experience in applying data science, ML techniques to solve business problems
  • Experience with LLMs and Prompt Engineering techniques
  • 1+ year of experience working with Gen AI solutions / LLMs such as GPT, Claude, Llama etc.
  • Solid background in NLP, Generative AI and hands-on experience and solid understanding of Machine Learning and Deep Learning methods and familiar with large language models

Other signals

  • Develop end-to-end ML/AutoML/AutoNLP pipelines and operationalize the end-to-end orchestration of the ML models
  • Build both batch and real-time model prediction pipelines
  • Experience with LLMs and Prompt Engineering techniques
  • Experience with Gen AI solutions / LLMs such as GPT, Claude, Llama etc.
  • Solid background in NLP, Generative AI and hands-on experience and solid understanding of Machine Learning and Deep Learning methods and familiar with large language models