We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Applied AI/ML Lead at JPMorgan Chase within the Asset and Wealth Management, you will provide deep engineering expertise and work across agile teams to enhance, build and deliver trusted market-leading technology products in a secure, stable, and scalable way. You will leverage your deep expertise to consistently challenge the status quo, innovate for business impact, lead the strategic development behind new and existing products and technology portfolios, and remain at the forefront of industry trends, best practices and technological advances. This role will focus on establishing and nurturing common capabilities, best practices, and reusable frameworks, creating a foundation for AI excellence that accelerates innovation and consistency across business functions.

 

Job responsibilities

 

Establishes and promotes a library of common ML assets, including reusable ML models, features stores, data pipelines, and standardized templates.

Leads efforts to create shared tools and platforms that streamline the end-to-end ML lifecycle across the organization.

Creates curative solutions using GenAI workflows through advanced proficiency in large language models (LLMs) and related techniques.

Gains Experience with creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines

Advises on the strategy and development of multiple products, applications, and technologies.

Leads advisor on the technical feasibility and business need for AIML use cases.

Liaises with firm wide AI ML stakeholders.

Translates highly complex technical issues, trends, and approaches leadership to drive the firm's innovation and enable leaders to make strategic, well informed decisions about technology advancements.

Influences across business, product and technology teams and successfully manages senior stakeholder relationships.

Champions the firm's culture of diversity, opportunity, inclusion, and respect.

 

Required qualifications, capabilities, and skills

Formal training or certification on Machine Learning concepts and 5+ years applied experience. MS and/or PhD in Computer Science, Machine Learning, or a related field and practical cloud native experience such as AWS needed.Experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.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.Get Hands on code and design to bring the experimental results into production solutions by collaborating with engineering team. Good understanding in deep learning frameworks such as PyTorch or TensorFlow.Experience in advanced applied ML areas such as GPU optimization, fine-tuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search).Ability to work on system design from ideation through completion with limited supervision.Passion for detail and follow through. Excellent communication skills and team playerDemonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.
 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.

Advanced knowledge in Reinforcement Learning or Meta Learning.

Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.

Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc.