Lead Software Engineer - Full Stack Ai/ml

JPMorgan Chase JPMorgan Chase · Banking · LONDON, LONDON, United Kingdom · Corporate Sector

Lead Software Engineer focused on building AI-enabled features for Data Center Services, including anomaly detection, capacity planning, NLP, and knowledge retrieval. Requires strong full-stack experience with Angular and NodeJS, and understanding of AI/ML concepts.

What you'd actually do

  1. Design and deliver AI enabled features for Data Center Services, including time series anomaly detection, predictive capacity planning, ticket NLP, and knowledge retrieval.
  2. Implements creative 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.
  3. Develops secure high-quality production code and collaborates with team members to ensure code quality and consistency.
  4. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
  5. Leads evaluation sessions with internal and external teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.

Skills

Required

  • software engineering concepts
  • system design
  • application development
  • testing
  • operational stability
  • frontend development with Angular
  • state management leveraging NGRX
  • functional and integration tests for Angular applications
  • backend development with NodeJS
  • Software Development Life Cycle
  • mock-up designs using tools such as Figma
  • agile ceremonies
  • cloud
  • artificial intelligence
  • machine learning
  • mobile

Nice to have

  • Computer vision experience (OpenCV and deep learning frameworks)
  • Model evaluation frameworks and automated test sets for both classical ML and LLMs
  • Event Driven Architecture (Kafka)
  • MySQL

What the JD emphasized

  • AI enabled features
  • time series anomaly detection
  • predictive capacity planning
  • ticket NLP
  • knowledge retrieval
  • artificial intelligence
  • machine learning

Other signals

  • AI enabled features
  • time series anomaly detection
  • predictive capacity planning
  • ticket NLP
  • knowledge retrieval