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Applied AI ML Lead-Python, LLM & Agentic AI
lead · Technology / Software Development
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Machine Learning Lead Software Engineer at JPMorgan Chase within the Regulatory, Controls, and Operational Risk Technology (RCORT) 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.
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 Agentic AI services, developing end-to-end AIML pipelines, Your expertise in Python, LLM ,Agentic Development and ML Ops will be crucial in this role.
Job responsibilities
- Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases.
- Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
- Manage, mentor, and guide a team of ML and MLOps engineers.
- Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
- Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation.
- Conduct thorough evaluations of generative models (e.g., GPT-5.x), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
- Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.
- Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.
- 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
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- Demonstrated experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production.
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs.
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications.
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering.
- Familiarity with the financial services industries.
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.