jpmorgan-chase-%26-co. - Bengaluru, Karnataka, India | 2024-09-06 11:53:35

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

 

Job Responsibilities:

• As a Machine Learning Engineer in the banking domain, you will be responsible for designing and implementing end to end machine learning solutions for production environment to solve complex problems related to personalized financial services in retail and digital banking domain. You will work closely with other fellow Machine Learning practitioners and cross-functional teams to translate business requirements into technical solutions and drive innovation in our banking products and services.
• You will collaborate with Machine Learning engineers, product managers, key business stakeholders, engineering and platform partners to deploy projects that delivers cutting edge machine learning driven digital solutions.
• You will write codes to create several machine learning experimentation pipelines.
• You will implement feature engineering pipelines and push features to feature stores. You will collaborate with data engineers and product analysts to preprocess and analyse large datasets from multiple sources.
• You will execute experiments and validations at scale, review results with Lead and Products.
• You will be responsible to create model serving pipelines that meets consumption SLAs.
• You will be responsible for writing production grade code for both training and inference functions. 
• You will collaborate with MLOps engineers in developing and testing the training and inference applications under production architecture blueprint often in integration with upstream and downstream applications.
• You will collaborate with MLOps engineers to register the models artifacts, maintain code repos, prepare for CI/CD execution and post production monitoring set ups.
• You will communicate and collaborate with Platform and Engineering partners to bring in the latest advancements in order to improve the scale, consistency, reliability and trustworthiness of the ML solutions

 

Required qualifications, capabilities and skills:

• BS, MS or PhD degree in Computer Science, Statistics, Mathematics or Machine learning related field.
• Proficiency in implementing ML models at least one of the following areas: Natural Language Processing, Knowledge Graph, 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, Statistics.
• Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn.
• Strong programming knowledge of python, spark; Strong coding knowledge on vector operations using numpy, scipy; 
• Strong analytical and critical thinking skills for problem solving.
• Excellent written and oral communication along with demonstrated teamwork skills.
• Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences.
• Experience of collaborating with other researchers, engineers, and stakeholders.
• A strong desire to stay updated with the latest advancements in the field and continuously improve one`s skills.

 

Preferred qualifications, capabilities and skills:

• 2+ (BS), 1+ (MS) or fresh ( out of PhD) years of relevant experience.
• Knowledge of distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc. Knowledge of distributed data/feature engineering using popular cloud services like AWS EMR. 
• Knowledge of large scale training, validation and testing experiments. Knowledge of cloud Machine Learning services in AWS i.e. Sagemaker
• Knowledge of container technology like Docker, ECS etc.. Knowledge of Kubernetes based platform for Training or Inferencing

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