Senior Lead Software Engineering - Python, Cloud, AI ML

JPMorgan Chase JPMorgan Chase · Banking · LONDON, LONDON, United Kingdom · Commercial & Investment Bank

Senior Lead Software Engineer role focused on building and deploying ML solutions into production within an enterprise environment. The role involves creating cloud-based frameworks for hosting ML models, integrating AIML solutions, and ensuring production readiness. Requires strong Python and AWS skills, collaboration with Data Scientists, and experience with the full model development lifecycle.

What you'd actually do

  1. Develop and maintain high-quality, secure applications using Python and AWS
  2. Create architecture and design deliverables, lead design and architecture reviews, own end-to-end delivery
  3. Integrate AIML solutions into complex, domain-specific operations processing systems
  4. Lead code reviews, design discussions, and agile planning sessions
  5. Collaborate with SRE and production monitoring teams to ensure system reliability and performance

Skills

Required

  • Python application development
  • developing, debugging and maintaining production applications
  • software development best practices
  • version control
  • testing
  • CI/CD
  • problem-solving
  • communication
  • collaboration
  • AIML systems
  • collaborating with data scientists
  • designing, building, and delivering maintainable, extensible applications into production environments

Nice to have

  • Cloud services
  • Infrastructure as Code (IaC)
  • containerized application development
  • relational databases
  • Postgres
  • AWS services
  • S3
  • EKS
  • SageMaker
  • Bedrock
  • Kubernetes
  • Docker
  • Kafka
  • MLOps
  • LLMs

What the JD emphasized

  • engineering standards
  • AIML solutions
  • production environments

Other signals

  • deploying innovative ML solutions into production
  • build cloud-based frameworks for hosting machine learning models
  • software engineering expertise throughout the model development lifecycle
  • ensure models meet SDLC standards, are production-ready, and can be deployed efficiently