Posted by JPMorgan Chase (JPMC) is a leading global financial services firm with assets of $2 trillion and operations in more than 60 countries. JPMC has made a strategic decision to fundamentally transform its operations through the adoption of artificial intelligence and machine learning at scale.
Backend Engineer – AI Platform
Job Description:
Collaborate with all of JPMorgan’s lines of business and functions to delivery software solutions.
Architect, design, and build core data backend systems and machine learning platforms to make huge technology and business impact.
Lead a team of engineers to build products and deliver solutions, depending on previous architecture and technical leadership experience.
Design and develop high-volume, fault-tolerant, scalable backend systems that process data and serve machine learning requests.
Evaluate various cloud platform technologies for big data and machine learning tasks. Architect and build features in the platform to incorporate these systems.
Determine the suitability and integrate various open source AI and ML libraries for large scale distributed model training on structured and unstructured data sets.
Work with various technologist and business stakeholders to architect and build JP Morgan’s unified cloud data lake.
Design and build backend processes for a machine learning dashboard to facilitate platform monitoring and collaboration between data scientists.
Minimum Qualifications
BS, MS or PhD degree in Computer Science or related quantitative field.
Solid programming skills with C/C++, Java, Python or other equivalent languages.
Cloud computing: Google Cloud, Amazon Web Service, Azure, Docker, Kubernetes.
Experience in distributed system design and development.
Big data technologies: Hadoop, Hive, Spark, Kafka.
Experience in ETL pipelines, both batch and real-time data processing.
Familiar with at least one MVC framework to build web services.
Self-motivation, great communication skills and team player.
Preferred Qualifications
Some knowledge in Machine Learning, Data Mining, Information Retrieval, Statistics..
Major machine learning frameworks: Tensorflow, Caffe/Caffe2, Pytorch, Keras, MXNet, Scikit-Learn.
General knowledge of machine learning platforms: SageMaker, Kubeflow, Domino, DataRobot, DriverlessAI