Applied AI ML Senior Associate

We have an exciting and rewarding opportunity for you to take your AI ML career to the next level.

As an Applied AI ML Senior Associate at JPMorgan Chase within the Commercial Bank Technology Team, you will leverage large-scale computation and scalable machine learning to uncover insight on the outputs of our grading models. Your expertise will promote the team to solve explainable problem, develop production prediction models, and manage ML Ops for building innovate systems that benefit our customers.

Job responsibilities :

• Develop solutions related to data architecture, ML Platform as well as GenAI platform architecture, provide tactical solution and design support to the team and embedded with engineering on  the execution and implementation of processes and procedures
• Serve as a subject matter expert on a wide range of ML techniques and optimizations.
• Provide in-depth knowledge of distributed ML platform deployment including training and serving.
• Create curative solutions using GenAI workflows through advanced proficiency in large language models (LLMs) and related techniques.
• Gain Experience with creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines.
• Get Hands on code and design to bring the experimental results into production solutions by collaborating with engineering team. 
• Own end to end code development in python/Java for both proof of concept/experimentation and production-ready solutions.
• Optimize system accuracy and performance by identifying and resolving inefficiencies and bottlenecks and collaborate with product and engineering teams to deliver tailored, science and technology-driven solutions.
• Drives decisions that influence the product design, application functionality, and technical operations and processes.

Required qualifications, capabilities, and skills :

• Formal training or certification on AI ML concepts and 3+ years applied experience 
• Solid understanding of using ML techniques specially in Natural Language Processing (NLP) and Large Language Models (LLMs)
• Hands-on experience with machine learning and deep learning methods.
• Good understanding in deep learning frameworks such as PyTorch or TensorFlow.
• Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search).
• Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.
• Practical cloud native experience such as AWS

Preferred qualifications, capabilities, and skills :

• Experience with Ray, MLFlow, and/or other distributed training frameworks.
• In-depth understanding of Embedding based  Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
• Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker.