Applied AI ML Lead at JPMC Candidate Experience page

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

11 Jun, 26

Salary

0.0

Posted On

13 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Sagemaker, Vertex Ai, Spark, Emr, Ray, Python, Java, Docker, Kubernetes, Ecs, Airflow, Kubeflow, Ec2, Iam, Model Training, Model Serving

Industry

Financial Services

Description

Our product, Personalization and Insights, builds and supports high throughput, low latency applications which leverage state of the art machine learning architectures, and which are deployed in AWS. These applications power personalized experiences across Chase Consumer & Community Banking channels, to help weave a user experience that includes traditional banking services with other services in the Travel, Merchant Offer Shopping, and Dining spaces. As Applied AI ML Lead in Consumer and Community Banking, Personalization and Insights Team, you will build and maintain pipelines for model training, batch/real-time model serving, hyperparameter tuning at scale, model monitoring, production validation and other activities vital for model development, testing and deployment in a well-managed, controlled environment. Job responsibilities * Deploy and maintain infrastructure (e.g., Sagemaker Notebooks) for providing an effective model development platform for data scientists and ML engineers that integrates with enterprise data ecosystem * Build, deploy and maintain ingress/egress and feature generation pipelines to calculate input features for model training and inference * Deploy and maintain infrastructure for batch and real-time model serving, in high throughput, low latency applications, at scale. * Identify, deploy and maintain high quality model monitoring and observability tools * Deploy and maintain infrastructure for compute intensive tasks such as hyperparameter tuning and interpretability and explainability * Partner with product, architecture, and other engineering teams to define scalable and performant technical solutions. * Leverage deep technical expertise to design extensible and scalable solutions, and to coach and grow individuals and teams. * Ensure team executes work according to compliance standards, SLAs, and business requirements, to meet the objectives of an initiative. Anticipates the needs of broader teams and potential dependencies with other teams. * Identify and mitigate issues to execute a book of work while escalating issues as necessary. * Proactively helps maintain high operational excellence standards for our production systems. Encourages development of technological methods and techniques within team. Required qualifications, capabilities, and skills * BS degree in Computer Science or related Engineering field * 7+ years applied experience * Deep experience and passion in model training, build, deployment and execution ecosystem such as Sagemaker and/or Vertex AI * Experience in monitoring and observability tools to monitor model input/output and features stats * Operational experience in big data tools such as Spark, EMR, Ray * Experience and interest in ML model architectures—linear/logistic regression, Gradient Boosted Trees, Neural Network architectures * Solid grounding in engineering fundamentals and analytical mindset. Bias for action and iterative development * Programming languages: Python, some Java * Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc.] * Solid fundamentals and experience with cloud technologies—EC2, Sagemaker, IAM. Good knowledge of Databases Preferred qualifications, capabilities, and skills * Experience with recommendation and personalization systems is a plus. JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs [https://careers.jpmorgan.com/us/en/how-we-hire/faqs] for more information about requesting an accommodation. Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

Responsibilities

The role involves building and maintaining pipelines for model training, batch/real-time serving, hyperparameter tuning, monitoring, and production validation for high throughput, low latency ML applications deployed in AWS. Responsibilities also include deploying and maintaining the necessary infrastructure for model development platforms and serving capabilities at scale.