Job Title: ML Engineer (AI/LLM & Cloud)

We are seeking a highly experienced ML Engineer to design, build, deploy, and integrate enterprise-scale AI/ML solutions within the JPMorgan Chase ecosystem. This role focuses on engineering production-ready machine learning applications, Large Language Model (LLM) solutions, and cloud-native ML platforms while partnering closely with Data Science teams and business stakeholders., * Design, develop, deploy, and maintain scalable AI/ML applications.

• Build production-ready machine learning systems and infrastructure.
• Productionize machine learning models developed by Data Science teams.
• Design and deploy Large Language Model (LLM) applications.
• Integrate AI solutions into AWS and JPMorgan Chase internal cloud platforms.
• Develop scalable model serving and inference pipelines.
• Implement CI/CD pipelines for ML applications.
• Build monitoring, observability, model drift detection, and automated retraining solutions.
• Optimize AI applications for performance, scalability, reliability, and cost.
• Collaborate with Product Managers, Data Scientists, Software Engineers, and Business SMEs.
• Mentor junior engineers and provide technical leadership through architecture guidance and code reviews.
• Research and implement modern AI/ML technologies and best practices.

The ideal candidate is a hands-on engineer with strong Python expertise, cloud experience, and MLOps knowledge who can lead technical initiatives and deliver scalable AI solutions., * Bachelor’’s or Master’’s degree in Computer Science, Engineering, Data Science, or a related field.

• 10+ years of hands-on experience developing and deploying machine learning solutions in production.
• Expert-level programming experience with Python.
• Basic understanding of Java or Scala.
• Strong experience with software engineering principles, data structures, algorithms, and distributed systems.
• Extensive experience with AWS Cloud.
• Experience deploying applications across public cloud and enterprise cloud platforms.
• Hands-on experience with Docker and Kubernetes.
• Strong experience with MLOps tools including MLflow, Kubeflow, SageMaker, or Vertex AI.
• Experience implementing CI/CD pipelines for machine learning applications.
• Experience designing scalable ML infrastructure and distributed data processing systems.
• Proven ability to lead technical initiatives and mentor engineering teams., * Experience building and deploying Large Language Model (LLM) solutions.
• Hands-on experience with Amazon Bedrock.
• Experience with Generative AI applications.
• Strong understanding of TensorFlow, PyTorch, or Scikit-learn.
• Experience with Deep Learning, NLP, or Computer Vision.
• Experience with distributed model training and high-throughput inference systems.
• Knowledge of model optimization and AI performance tuning.