Vice President, MLOps Engineering Lead

4 weeks ago

Plano, United States JPMorgan Chase & Co. Full time

As a Vice President, MLOps Lead in our technology team, you will have the opportunity to solve exciting business problems in the domain of commercial banking, payments, and financial services. We expect you to have a strong curiosity for data and a proven track record of successfully applying rigorous scientific methods with proficiency in Machine Learning Engineering and DevOps capabilities. 

In this role, you will apply your strong knowledge Kubernetes, Sage Maker, and experience working with massive amounts of data. You will also utilize your strong software engineering skills having MLOps and LLMOps to build systems that reach JP Morgan scale. This role provides a unique opportunity to apply your skills and have a direct impact on global business.

Job   Responsibilities

Use large scale data processing frameworks such as Spark, AWS EMR for feature engineering and be proficient across various data both structured and un-structured. Build ML models across Public and Private clouds including container-based Kubernetes environments. Develop end-to-end ML pipelines necessary to transform existing applications and business processes into true AI systems. Build both batch and real-time model prediction pipelines with existing application and front-end integrations.  Collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and/or cause and effect relations.

Required qualifications, capabilities and skills

6+ years of experience with expertise in building and deploying production-grade machine learning (ML) and large language model (LLM) models on large-scale datasets Proficiency in leveraging large-scale data processing frameworks like Spark and AWS EMR for feature engineering, working with both structured and unstructured data Ability to build ML and LLM models that can be deployed across public and private clouds, including container-based Kubernetes environments like EKS Experience in developing end-to-end ML and LLM pipelines to transform existing applications and business processes into AI-powered systems Familiarity with building both batch and real-time model prediction pipelines with existing application and front-end integrations Expertise in Python, PySpark, and deep learning frameworks like TensorFlow, as well as proficiency in MLOps Familiarity with machine learning techniques and advanced analytics, including regression, classification, clustering, time series, econometrics, causal inference, and mathematical optimization

Preferred qualifications, capabilities and skills:

Experience working with end-to-end pipelines using frameworks like KubeFlow, TensorFlow, and/or crowd-sourced data labeling

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