Demo

AWS Software Engineer III, Machine Learning

JPMC Candidate Experience page
Wilmington, DE Full Time
POSTED ON 1/19/2025
AVAILABLE BEFORE 4/19/2025

AWS Software Engineer III, Machine Learning

Minimum Requirements

  • Formal training or certification on software engineering concepts and 3 years applied experience
  • Hands-on practical experience in system design, application development, testing, and operational stability
  • Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
  • Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
  • Hands-on experience in AWS-EC2, S3, EMR and Sagemaker to migrate the machine learning models from Hadoop to AWS
  • Good understanding and knowledge in planning and managing AWS Cloud infrastructure and enabling necessary platform security by using best-in-class Cloud Security Solutions
  • Hands-on experience in migrating Spark applications from Bigdata Hadoop platform to AWS Cloud using AWS EMR, S3 Buckets, EBS, Lambda and EC2 Instances
  • Hands-on experience in application development using EC2, S3, EMR, Lambda
  • Provision AWS resources into multiple cloud accounts through automated pipeline using Terraform
  • Experience in the AWS platform monitoring tools like Cloudwatch, Datadog for monitoring the utilization and in setting up the thresholds and performance alerts
  • Hands-on experience in developing automation scripts using Python and Unix Shell Scripting

Responsibilities

  • As a key member of an agile team, you'll manage model development infrastructure, contribute to the firm's goals, and advance your career in a vibrant environment
  • Execute software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  • Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
  • Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
  • Identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene and system architecture
  • Design and set up Machine Learning platform infrastructure to develop and train machine learning models for Risk and Fraud
  • Set up ML tools on AWS infrastructure through automated pipeline and using Terraform (Infrastructure as Code)
  • Design and develop tools to support machine learning platform for critical CCB Risk and Fraud models
  • Implement performance tuning EMR jobs and best practices in Spark job development
  • Enable distributed machine learning techniques on Xgboost, TensorFlow, Scikit-learn using EMR RAPIDS, Spark, Dask, and Sagemaker
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