Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity apply your skills and have a direct impact on global business. You will be building production-grade ML services, developing end-to-end ML pipelines, and collaborating to develop large-scale data modeling experiments. Your expertise in Python, PySpark, DL frameworks like TensorFlow, and MLOps will be crucial in this role.
As a Machine Learning Lead Software Engineer at JPMorgan Chase within the Corporate Oversight and Governance Technology AI/ML team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
1. Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases.
2. Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
3. Manage, mentor, and guide a team of ML and MLOps engineers.
4. Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
5. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation.
6. Conduct thorough evaluations of generative models (., GPT-4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
7. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.
8. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.
9. Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
Required qualifications, capabilities, and skills
10. Formal training or certification on Python and AI/ML engineering concepts and proficient advanced experience
11. Bachelor's or Master's degree in Computer Science, Engineering, or a related field
12. Demonstrated experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production.
13. Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
14. Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
15. Experience with cloud computing platforms (., AWS, Azure, or Google Cloud Platform), containerization technologies (., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
16. Solid understanding of fundamentals of statistics, machine learning (., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs.
17. Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications.
18. Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
19. A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering.
Preferred qualifications, capabilities, and skills
20. Familiarity with the financial services industries.
21. Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
22. Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.