Applied AI ML Lead

JPMorgan Chase JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Asset & Wealth Management

Lead role focused on designing, deploying, and managing prompt-based LLM models for NLP tasks in financial services. Involves research into prompt engineering, LLM orchestration, building data pipelines, and developing tools for model training, evaluation, and optimization, with a strong emphasis on MLOps and cloud deployment.

What you'd actually do

  1. Design, deploy and manage prompt-based models on LLMs for various NLP tasks in the financial services domain
  2. Conduct research on prompt engineering techniques to improve the performance of prompt-based models within the financial services field, exploring and utilizing LLM orchestration and agentic AI libraries.
  3. Build and maintain data pipelines and data processing workflows for prompt engineering on LLMs utilizing cloud services for scalability and efficiency.
  4. Develop and maintain tools and framework for prompt-based model training, evaluation and optimization
  5. Analyze and interpret data to evaluate model performance to identify areas of improvement

Skills

Required

  • software engineering concepts
  • prompt design and implementation or chatbot application
  • Python
  • PyTorch or TensorFlow
  • building data pipelines for both structured and unstructured data processing
  • developing APIs and integrating NLP or LLM models into software applications
  • cloud platforms (AWS or Azure) for AI/ML deployment and data processing
  • excellent problem-solving
  • communicate ideas and results to stakeholders and leadership
  • deployment processes
  • GIT and version control systems
  • LLM orchestration and agentic AI libraries
  • MLOps tools and practices

Nice to have

  • model fine-tuning techniques such as DPO and RLHF
  • Java
  • Spark
  • financial products and services including trading, investment and risk management

What the JD emphasized

  • prompt-based models on LLMs
  • prompt engineering techniques
  • LLM orchestration and agentic AI libraries
  • data pipelines and data processing workflows for prompt engineering on LLMs
  • tools and framework for prompt-based model training, evaluation and optimization
  • MLOps tools and practices

Other signals

  • prompt-based models on LLMs
  • prompt engineering techniques
  • LLM orchestration and agentic AI libraries
  • data pipelines and data processing workflows for prompt engineering on LLMs
  • tools and framework for prompt-based model training, evaluation and optimization
  • MLOps tools and practices