Applied Ai/ml [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Asset & Wealth Management

JPMorgan Chase is seeking an Applied AI/ML professional to execute AI/ML software solutions, perform design, development, and troubleshooting, create secure production applications, and maintain AI algorithms. The role involves producing ML and GenAI architecture components, gathering analysis, synthesizing data, and developing models for continuous improvement. Responsibilities include identifying patterns in data to drive improvements in ML model pipelines and system architecture, and contributing to AI/ML communities of practice.

What you'd actually do

  1. Execute artificial intelligence (AI) and machine learning (ML) software solutions.
  2. Perform design, development, and technical troubleshooting to build solutions and break down technical problems.
  3. Create secure production applications and maintain AI algorithms that run synchronously with appropriate systems.
  4. Produce ML and GenAI architecture components and design artifacts for applications, ensuring that design constraints are met by software application development.
  5. Gather analysis, and synthesize and develop models, integrations, visualizations, and reporting from large, diverse data sets in service of continuous improvement of software applications and systems.

Skills

Required

  • Python
  • AWS
  • Azure
  • Kubernetes
  • Terraform
  • Docker
  • EKS
  • ECS
  • SNS
  • SQS
  • S3
  • CI/CD
  • Agile development
  • Java
  • scikit-learn
  • XGBoost
  • decision trees
  • collaborative filtering
  • content-based filtering
  • feature engineering
  • label engineering
  • data cleaning
  • exploratory data analysis
  • text classification
  • sentiment analysis
  • entity extraction
  • front-end plugins
  • logging systems
  • alerting
  • monitoring tools
  • microservice architecture
  • vector databases
  • context engineering
  • semantic similarity
  • BM25
  • OpenSearch
  • FinOS AI Governance Framework
  • transformer-based models
  • summarization
  • question answering

Nice to have

  • GenAI architecture
  • MLOps
  • deep learning

What the JD emphasized

  • Master's degree in Computer Science, Computer Engineering, Information Technology, Data Science, or related field of study plus 3 years of experience
  • Bachelor's degree in Computer Science, Computer Engineering, Information Technology, Data Science, or related field of study plus 5 years of experience
  • Programming machine learning solutions using Python
  • Developing software across front-end and back-end systems using cloud services such as AWS or Azure
  • Developing and deploying AI/ML solutions using Kubernetes, Terraform, Docker, and AWS Cloud Services including EKS, ECS, SNS, SQS, and S3
  • Implementing CI/CD pipelines using agile development frameworks
  • Building scalable developer and business-facing financial applications using Java
  • Building and optimizing regression and classification models using scikit-learn, XGBoost, and decision trees
  • Developing personalized recommendation engines using collaborative and content-based filtering
  • Performing feature engineering, label engineering, data cleaning, and exploratory data analysis for machine learning readiness
  • Executing natural language processing using text classification, sentiment analysis, and entity extraction
  • Building developer and content generation platforms using front-end plugins, logging systems, alerting, and monitoring tools
  • Designing machine learning and neural search applications using microservice architecture, vector databases, and context engineering including semantic similarity, BM25, and OpenSearch
  • Applying AI and ML governance frameworks and compliance standards such as the FinOS Al Governance Framework in the financial industry
  • Building and fine-tuning transformer-based models using summarization and question answering

Other signals

  • building and deploying AI/ML solutions
  • building scalable developer and business-facing financial applications
  • building and optimizing regression and classification models
  • developing personalized recommendation engines
  • executing natural language processing
  • designing machine learning and neural search applications
  • applying AI and ML governance frameworks and compliance standards
  • building and fine-tuning transformer-based models