Applied AI ML Associate

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

This role focuses on designing, deploying, and managing prompt-based LLM models for NLP tasks in financial services. It involves research into prompt engineering, utilizing LLM orchestration and agentic AI libraries, building data pipelines for LLMs, and developing tools for model training and evaluation. The role requires strong Python skills, experience with ML frameworks, cloud platforms, and MLOps.

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

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Experience with prompt design and implementation or chatbot application
  • Strong programming skills in Python with experience in PyTorch or TensorFlow
  • Experience building data pipelines for both structured and unstructured data processing.
  • Experience in developing APIs and integrating NLP or LLM models into software applications
  • Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing.
  • Excellent problem-solving and the ability to communicate ideas and results to stakeholders and leadership in a clear and concise manner
  • Basic knowledge of deployment processes, including experience with GIT and version control systems
  • Familiarity with LLM orchestration and agentic AI libraries
  • Hands on experience with MLOps tools and practices, ensuring seamless integration of machine learning models into production environment

Nice to have

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

What the JD emphasized

  • prompt-based models on LLMs
  • prompt engineering techniques
  • LLM orchestration
  • agentic AI libraries
  • prompt-based model training, evaluation and optimization

Other signals

  • LLM orchestration
  • prompt engineering
  • agentic AI