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

As an Applied AI ML Lead within the Digital Intelligence team at JPMorgan, you will collaborate with all lines of business and functions to deliver software solutions. You will have the opportunity to experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a significant impact on technology and business. You will design and implement highly scalable and reliable data processing pipelines, perform analysis and insights to promote and optimize business results. This role provides an opportunity to contribute to a transformative journey and make a substantial impact on a wide range of customer products.

Job Responsibilities:

Design and implement end-to-end machine learning solutions for the production environment to solve complex problems related to personalized financial services in retail and digital banking. Work closely with other Machine Learning practitioners and cross-functional teams to translate business requirements into technical solutions and drive innovation in our banking products and services. Collaborate with Machine Learning engineers, product managers, key business stakeholders, engineering, and platform partners to deploy projects that deliver cutting-edge machine learning-driven digital solutions. Write code to create machine learning experimentation pipelines and design feature engineering pipelines to push to feature stores. Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources. Execute experiments and validations at scale, and review results with Lead and Products. Create model serving pipelines that meet consumption SLAs and write production-grade code for both training and inference functions. Collaborate with MLOps engineers to develop and test training and inference applications under the production architecture blueprint, integrating with upstream and downstream applications, while also registering model artifacts, maintaining code repositories, and preparing for CI/CD execution and post-production monitoring. Drive end-to-end system architecture in collaboration with ML, MLOps, and Architecture leads. Communicate and collaborate with Platform and Engineering partners to bring in the latest advancements to improve the scale, consistency, reliability, and trustworthiness of the ML solutions Mentor junior Machine Learning associates in delivering successful projects and building careers, while also contributing to firm-wide ML communities through patenting, publications, and speaking engagements.
 

 Required qualifications, capabilities and skills: 

BS, MS, or PhD degree in Computer Science, Statistics, Mathematics, or a related field in Machine Learning. Expert proficiency in implementing ML models in at least one of the following areas: Natural Language Processing, Knowledge Graphs, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. Foundational knowledge in data structures, algorithms, machine learning, data mining, information retrieval, and statistics. Demonstrated expertise in machine learning frameworks such as TensorFlow, PyTorch, PyG, Keras, MXNet, and Scikit-Learn. Expert programming knowledge in Python and Spark; expert coding knowledge of vector operations using NumPy and SciPy. Coding knowledge in distributed computation using multithreading, multi-GPUs, Dask, Ray, Polars, etc. Strong analytical and critical thinking skills for problem-solving. Excellent written and oral communication skills, along with demonstrated teamwork abilities. Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences. Experience working with interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders.

Preferred qualifications, capabilities and skills:

Familiarity with distributed data and feature engineering using popular cloud services like AWS EMR. Exposure to large-scale training, validation, and testing experiments. Experience with cloud Machine Learning services in AWS, such as SageMaker. Understanding of container technologies like Docker and ECS. Knowledge of Kubernetes-based platforms for training or inferencing.