What are the responsibilities and job description for the Applied AI ML Engineer-Vice President position at JPMorgan Chase?
The Corporate & Investment Bank is a global leader across investment banking, wholesale payments, markets and securities services. The world’s most important corporations, governments and institutions entrust us with their business in more than 100 countries. We provide strategic advice, raise capital, manage risk and extend liquidity in markets around the world.
As an Applied AI/ML 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. with 2-3 years experience or Masters with 4-5 years experience 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