Applied AI ML Senior Associate, Chief Data & Analytics Office

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Corporate Sector

Lead the design, development, and deployment of advanced AI, GenAI, and LLM solutions in an enterprise setting. Focus on integrating generative AI, building scalable production systems, and ensuring responsible AI practices. Requires strong Python and ML framework experience, understanding of agentic workflows, and cloud/MLOps proficiency.

What you'd actually do

  1. Lead the hands-on design, development, and deployment of advanced AI, GenAI, and large language model solutions.
  2. Serve as a subject matter expert on a wide range of machine learning techniques and optimizations.
  3. Collaborate with product, engineering, and business teams to deliver scalable, production-ready AI systems.
  4. Conduct experiments using the latest ML technologies, analyze results, and tune models for optimal performance.
  5. Own end-to-end code development in Python for both proof-of-concept and production-ready solutions.

Skills

Required

  • Master's or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field.
  • 3+ years of hands-on experience in applied machine learning (GenAI, LLMs, foundation models).
  • Python programming
  • ML frameworks (PyTorch, TensorFlow)
  • Designing, training, and deploying large-scale ML/AI models in production
  • Prompt engineering
  • Agentic workflows
  • Orchestration frameworks
  • Cloud platforms (AWS, Azure, GCP)
  • Distributed systems (Kubernetes, Ray, Slurm)
  • MLOps tools and practices (MLflow, model monitoring, CI/CD for ML)
  • Communication skills

Nice to have

  • High-performance computing and GPU infrastructure (NVIDIA DCGM, Triton Inference)
  • Big data processing tools
  • Cloud data services
  • Building and deploying ML models on AWS Sagemaker, EKS

What the JD emphasized

  • Minimum 3 years of hands-on experience in applied machine learning, including generative AI, large language models, or foundation models.
  • Proven experience designing, training, and deploying large-scale ML/AI models in production environments.
  • Understanding of prompt engineering, agentic workflows, and orchestration frameworks.
  • Ensure responsible AI practices, model governance, and compliance with regulatory standards.

Other signals

  • deployment of innovative AI and machine learning solutions
  • address complex business challenges
  • deliver scalable, production-ready AI systems
  • integrate generative AI within the ML platform
  • responsible AI governance and compliance