Software Engineer II - Applied AI

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

Software Engineer II - Applied AI at JPMorgan Chase in London. This role focuses on developing and deploying advanced machine learning models and generative AI applications/agents, with a strong emphasis on MLOps, AWS deployment, and integrating user feedback for self-improving systems. The role involves architecting AI solutions, analyzing data, ensuring scalability and reliability, and staying updated on AI advancements.

What you'd actually do

  1. Development of advanced machine learning models to address complex operational challenges.
  2. Evaluating and communicating the impact brought by proposed solutions.
  3. Architect and oversee the deployment of generative AI applications and agents to automate and enhance business processes.
  4. Collaborate with senior stakeholders to understand strategic business needs and translate them into comprehensive technical solutions.
  5. Analyze large datasets to extract actionable insights and support data-driven decision-making at a strategic level.

Skills

Required

  • AI/ML model development
  • deployment of AI/ML applications in a production environment
  • AWS platforms
  • MLOps practices
  • TensorFlow
  • PyTorch
  • PyTorch Lightning
  • Scikit-learn
  • Python
  • generative AI models
  • OpenAI
  • integrating user feedback
  • data preprocessing
  • feature engineering
  • model evaluation techniques
  • AWS
  • problem-solving skills
  • communication skills

Nice to have

  • Ph.D.
  • software engineering practices
  • agentic frameworks
  • fine-tuning LLMs
  • prompt optimisation
  • AI application architecture
  • strategic impact
  • innovation

What the JD emphasized

  • deployment of AI/ML applications in a production environment
  • MLOps practices
  • integrating user feedback to establish self-improving AI applications
  • bringing AI applications to production

Other signals

  • development of advanced machine learning models
  • deployment of generative AI applications and agents
  • MLOps practices
  • integrating user feedback to establish self-improving AI applications