Applied AI ML Lead [Multiple Positions Available]

JPMorgan Chase & Co.•Palo Alto, CA

6h•Onsite

About The Position

This role involves designing, building, and deploying machine learning (ML) and natural language processing (NLP) techniques for text data, focusing on classification, summarization, and sentiment analysis. The position requires the development and maintenance of comprehensive data pipelines and frameworks for preprocessing, deployment, monitoring, and management of ML applications. Key responsibilities include monitoring performance, documenting methodologies, defining the architectural roadmap for applied AI and large language model (LLM) initiatives, and leading/mentoring a team of AI and ML engineers. The role also involves conducting code and system design reviews, acting as a primary escalation point for technical issues related to LLM deployment, and architecting robust Generative AI (GenAI) applications by integrating LLMs into secure, scalable systems. Directing the design and development of GenAI and Agentic solutions using prompt engineering and RAG, leading end-to-end development and fine-tuning of custom LLMs, and integrating/fine-tuning AI models for production deployment are also crucial. The lead will oversee project timelines, collaborate with product managers to translate business needs into technical requirements, and drive the long-term AI strategy by identifying new opportunities and communicating complex technical concepts.

Requirements

Master's degree in Computer Science, Mathematics, Statistics, Data Science or related field of study plus three (3) years of experience in the job offered or as Applied AI ML Lead, Applied AI/ML Engineer, Data Scientist, Quantitative Analyst, or related occupation.
Alternatively, a Bachelor's Degree in Computer Science, Mathematics, Statistics, Data Science or related field of study plus five (5) years of experience in the job offered or as Applied AI ML Lead, Applied AI/ML Engineer, Data Scientist, Quantitative Analyst, or related occupation.
Applying NLP techniques including tokenization, embedding, and training transformer models for tasks including classification and entity recognition (3 years experience).
Building, training, and deploying ML and Deep Learning models with TensorFlow, PyTorch and HuggingFace, including fine-tuning neural network based-models (3 years experience).
Designing and implementing advanced retrieval systems using vector databases including OpenSearch (3 years experience).
Using metrics including accuracy, precision, and recall testing and evaluating the performance of ML models and AI applications (3 years experience).
Designing, deploying, and monitoring AI and ML systems in production with CI/CD, model versioning, reliability, governance, and cost optimization (3 years experience).
Using cloud including AWS and services including ECS and SageMaker to build and deploy enterprise AI and ML solutions (3 years experience).
Writing Python code for AI and ML workflows, data preprocessing, and feature engineering utilizing libraries including NumPy, Scikit-Learn, PySpark, and Pandas for data manipulation (3 years experience).
Using SQL for data transformation and feature engineering (3 years experience).
Using RESTful API design and implementation (3 years experience).
Building LLM and multi-modal AI solutions, including multi-step agents and generative models for text and image tasks, using frameworks including LangChain (1 year experience).
Designing and refining prompts for generative AI, architecting multi-agent systems, and implementing ethical and safety guardrails in AI development (1 year experience).

Responsibilities

Design, build and deploy machine learning (ML) and natural language processing (NLP) techniques to text data for classification, summarization and sentiment analysis.
Design, develop, and maintain comprehensive data pipelines and frameworks for data preprocessing and streamlined deployment, monitoring, and management of ML applications.
Monitor performance and document methodologies and logs to drive improvements and ensure reproducibility.
Define the architectural roadmap and technical direction for applied AI and large language model (LLM) initiatives.
Lead and mentor a team of AI and ML engineers on GenAI application delivery and best practices across the full development lifecycle.
Conduct code and system design reviews, ensuring quality, security, and maintainability.
Act as the primary escalation point for complex technical issues related to LLM application deployment and integration.
Deploy and architect robust GenAI applications by integrating LLMs into secure, scalable, and reliable systems for orchestrating workflows in high-stakes environments.
Direct the design and development of generative AI and Agentic solutions using prompt engineering, RAG, and relevant frameworks.
Lead end-to-end development and fine-tuning of custom LLMs and GenAI models, including data preparation and techniques like Low-Rank Adaptation.
Integrate, fine-tune, and evaluate custom or off-the-shelf LLMs and other AI models and tools for production deployment.
Oversee project timelines, resources, and deliverables for LLM projects.
Collaborate with product managers and stakeholders to translate business needs into technical requirements.
Drive the long-term AI strategy by identifying new opportunities and communicating complex technical concepts to diverse audiences.