Applied AI ML Lead [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Palo Alto, CA +1 · Commercial & Investment Bank

Lead and mentor a team of AI/ML engineers in designing, building, and deploying GenAI and LLM applications, including custom model fine-tuning and agentic solutions, within a secure and scalable enterprise environment. Focus on end-to-end development, integration, and production deployment, with responsibilities spanning data pipelines, model evaluation, and architectural roadmapping.

What you'd actually do

  1. Lead and mentor a team of AI and ML engineers on GenAI application delivery and best practices across the full development lifecycle.
  2. Deploy and architect robust GenAl applications by integrating LLMs into secure, scalable and reliable systems for orchestrating workflows in high-stakes environments.
  3. Direct the design and development of generative Al and Agentic solutions using prompt engineering, RAG, and relevant frameworks.
  4. Lead end-to-end development and fine-tuning of custom LLMs and GenAI models, including data preparation and techniques like Low-Rank Adaptation.
  5. Integrate, fine-tune, and evaluate custom or off-the-shelf LLMs and other AI models and tools for production deployment.

Skills

Required

  • Applying NLP techniques including tokenization, embedding, and training transformer models for tasks including classification and entity recognition
  • Building, training, and deploying ML and Deep Learning models with TensorFlow, PyTorch and HuggingFace, including fine-tuning neural network based-models
  • Designing and implementing advanced retrieval systems using vector databases including OpenSearch
  • Using metrics including accuracy, precision, and recall testing and evaluating the performance of ML models and AI applications
  • Designing, deploying, and monitoring AI and ML systems in production with CI/CD, model versioning, reliability, governance, and cost optimization
  • Using cloud including AWS and services including ECS and SageMaker to build and deploy enterprise Al and ML solutions
  • Writing Python code for Al and ML workflows, data preprocessing, and feature engineering utilizing libraries including NumPy, Scikit-Learn, PySpark, and Pandas for data manipulation
  • Using SQL for data transformation and feature engineering
  • Using RESTful API design and implementation
  • Building LLM and multi-modal AI solutions, including multi-step agents and generative models for text and image tasks, using frameworks including LangChain
  • Designing and refining prompts for generative AI, architecting multi-agent systems, and implementing ethical and safety guardrails in AI development

What the JD emphasized

  • high-stakes environments
  • secure, scalable, and reliable systems
  • orchestrating workflows

Other signals

  • LLM application delivery
  • GenAI application delivery
  • full development lifecycle
  • LLM application deployment and integration
  • GenAI applications
  • orchestrating workflows
  • generative AI and Agentic solutions
  • prompt engineering
  • RAG
  • end-to-end development and fine-tuning of custom LLMs
  • integrating, fine-tuning, and evaluating custom or off-the-shelf LLMs