Job Description

Join our team as a Vice President in our Applied AI ML function to advance AI in financial services and optimise business decisions.





As a Vice President Applied AI ML Scientist / Engineer in the Applied AI ML team at JPMorgan Commercial and Investment Bank, you'll be at the forefront of AI innovation, combining cutting-edge techniques with unique data assets to optimise business decisions and automate processes. This role offers a unique blend of scientific research and software engineering, allowing you to advance AI in financial services and build impactful products.





In this role, you will leverage the latest research in Natural Language Processing and statistical machine learning to build AI-powered products that automate processes and enhance decision-making. You will collaborate with software engineering teams to design scalable Machine Learning services and communicate AI capabilities to diverse audiences.

Job Responsibilities:

Lead the development and implementation of advanced machine learning models and algorithms to address complex operational challenges. Architect and oversee the deployment of generative AI applications and agents to automate and enhance business processes. Collaborate with senior stakeholders to understand strategic business needs and translate them into comprehensive technical solutions. Analyze large datasets to extract actionable insights and support data-driven decision-making at a strategic level. Ensure the scalability, reliability, and security of AI/ML solutions in a production environment, with a focus on long-term sustainability. Stay informed about the latest advancements in AI/ML technologies and drive their integration into our operations. Mentor and guide junior team members, fostering a culture of innovation and continuous learning.

Required Qualifications, Capabilities, and Skills:

Advanced degree in a STEM field, with significant experience in AI/ML. Proven track record of deploying AI/ML applications in a production environment, with expertise in deploying models on AWS platforms such as SageMaker or Bedrock. Deep familiarity with MLOps practices, covering 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 Lightning, or Scikit-learn. Proficiency in Python with a strong emphasis on code quality and reliability through comprehensive testing. Extensive experience with generative AI models, both as cloud service APIs (e.g., OpenAI) and open source (e.g., Huggingface). Experience with integrating user feedback to establish data flywheels and self-improving AI applications. Solid understanding of data preprocessing, feature engineering, and model evaluation techniques. Familiarity with cloud platforms (AWS). 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, Capabilities, and Skills:

A Ph.D. is a plus but not required. Experience in the financial services industry, particularly within investment banking operations. Experience in developing AI solutions using agentic frameworks. Experience fine-tuning LLMs with advanced techniques. Experience with prompt optimization to enhance the performance and effectiveness of prompt engineering. Demonstrated ability to design and implement AI application architecture. * Significant experience in bringing AI applications to production with a focus on strategic impact and innovation.