JPMorgan Applied Artificial Intelligence Machine Learning Lead VP 

United States, New York, New York 

116345913

As an AI/ML Data Scientist/Engineer, you will be responsible for designing, developing, and deploying cutting-edge AI and machine learning solutions to enhance the efficiency and effectiveness of our operations. You will work closely with cross-functional teams to identify opportunities for automation and process improvement, utilizing your expertise in machine learning and generative AI.

Job Responsibilities:

• Develop and implement machine learning models and algorithms to solve complex operational challenges.
• Design and deploy generative AI applications to automate and optimize business processes.
• Collaborate with stakeholders to understand business needs and translate them into technical solutions.
• Analyze large datasets to extract actionable insights and drive data-driven decision-making.
• Ensure the scalability and reliability of AI/ML solutions in a production environment.
• Stay up-to-date with the latest advancements in AI/ML technologies and integrate them into our operations.
• Mentor and guide junior team members in AI/ML best practices and methodologies.

Key Skills and Qualifications:

• Ph.D. in Computer Science, Data Science, Machine Learning, or a related field.
• Experience in deploying AI/ML applications in a production environment, with skills in deploying models on AWS platforms such as SageMaker or Bedrock.
• Familiarity with MLOps practices, encompassing the full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models.
• Expertise in machine learning frameworks such as TensorFlow, PyTorch, Pytorch lightening, or Scikit-learn.
• Proficiency in programming languages such as Python
• Proficiency in writing comprehensive test cases, with a strong emphasis on using testing frameworks such as pytest to ensure code quality and reliability.
• Experience with generative AI models, including GANs, VAEs, or transformers. Experience with Diffusion models is a plus.
• Solid understanding of data preprocessing, feature engineering, and model evaluation techniques.
• Familiarity with cloud platforms (AWS) and containerization technologies (Docker, Kubernetes, Amazon EKS).
• Excellent problem-solving skills and the ability to work independently and collaboratively.
• Strong communication skills to effectively convey complex technical concepts to non-technical stakeholders.

Preferred Qualifications:

• Experience in the financial services industry, particularly within investment banking operations.
• Experience in developing AI solutions using agentic frameworks.
• Experience fine-tuning SLMs with approaches like LoRA, QLoRA and DoRA.
• Experience with prompt optimization frameworks such as AutoPrompt and DSPY to enhance the performance and effectiveness of prompt engineering.
• Familiarity with distributed computing systems, frameworks and techniques like data sharding and DDP training