Software Engineer III - Applied AI

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

Software Engineer III - Applied AI at JPMorgan Chase, focused on developing and deploying generative AI applications and agents, with expertise in MLOps, AWS, and integrating user feedback for self-improving systems within the fintech domain.

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

  • Advanced degree in a STEM field (Degree in Computer Science or Software Engineering)
  • AI/ML
  • AI/ML model development and deployment
  • deploying models on AWS platforms
  • MLOps practices
  • TensorFlow
  • PyTorch
  • PyTorch Lightning
  • Scikit-learn
  • Python
  • generative AI models
  • cloud service APIs (e.g., 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
  • AI solutions using agentic frameworks
  • fine-tuning LLMs with advanced techniques
  • prompt optimisation
  • AI application architecture
  • bringing AI applications to production

What the JD emphasized

  • deployment of AI/ML applications in a production environment
  • deploying models on AWS platforms
  • MLOps practices
  • integrating user feedback to establish self-improving AI applications
  • generative AI applications and agents

Other signals

  • deployment of generative AI applications and agents
  • MLOps practices
  • deploying models on AWS platforms
  • integrating user feedback to establish self-improving AI applications