Applied AI ML [Multiple Positions Available]

JPMorgan Chase & Co.•New York, NY

1d•$183,000 - $260,000•Onsite

About The Position

This role involves driving the end-to-end modeling lifecycle, including defining objectives, selecting methodologies, performing quantitative analysis, and coding. The position requires coordinating cross-functional engineering teams to architect and implement scalable AI Agents, Agentic workflows, and Generative AI solutions. Responsibilities include developing presentations for senior management, ensuring adherence to model governance, change management, risk, and Responsible AI policies. The role also involves connecting with industry participants to stay updated on scientific research, adapting design principles, building vendor relationships, supporting external outreach, and mentoring junior team members.

Requirements

Master's degree in Data Science, Computer Science, or related field of study
Four (4) years of experience in the job offered or as Applied AI ML, Applied Scientist, Data Scientist, or related occupation.
Four (4) years of experience with Coding in Python, Java, C#, and R
Four (4) years of experience with Building, training, and deploying neural network architectures including Transformers, CNN, and LSTM using PyTorch, TensorFlow and JAX
Four (4) years of experience with Pre-training, fine-tuning, and deploying domain specific neural models using user transcript and conversational data
Four (4) years of experience with Developing real-time and high-throughput concurrent inference pipelines and deploying customized language models using EKS, ECS, and SageMaker
Four (4) years of experience with Implementing production facing code for Big Data analytics using OpenSearch, Spark, and EMR
Four (4) years of experience with Data warehousing and data lake management using Databricks, Snowflake, and S3
Four (4) years of experience with Designing and conducting statistical modeling and A/B testing to measure, assess, and optimize model performance
Four (4) years of experience with Architecting advance natural language search and retrieval system such as RAG, knowledge graph, or vector databases.
Two (2) years of experience with Modernizing legacy machine learning workflows and migrating to Gen AI solutions
Two (2) years of experience with Creating foundational capabilities to support, optimize, and scale Agentic AI solutions such as MCP, A2A, or multi-agent system.
One (1) year of experience with Enhancing transcription and phonetic errors robustness in downstream ASR and speech-to-text applications by leveraging multimodal data across audio, text, and image modalities such as Joint Latent Space Embeddings.

Responsibilities

Drive the end to end modeling lifecycle, including defining the objective and key decision variables, choosing appropriate methodologies, performing advanced quantitative analysis, and writing and maintaining code.
Coordinate cross-functional engineering teams to architect and implement scalable AI Agents, Agentic workflows, and Generative AI solutions.
Develop presentations to communicate key findings, business metrics, and strategic recommendations to senior management and stakeholders.
Responsible for adherence to model governance, change management, risk, and Responsible AI policies and procedures.
Connect with industry participants to remain aware of active scientific research and adapt to our design principles.
Build and Maintain vendor relationships and support external outreach efforts.
Mentor junior team members on building technical expertise.