AI ML Associate

As an Applied AIML Engineer at JPMorgan Chase within the Corporate Oversight and Governance Technology 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 firms business objectives.

Job responsibilities

• Work with product managers data scientists ML engineers and other stakeholders to understand requirements.
• Design develop and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
• 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) 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

• Formal training or certification on AIML engineering concepts and 3 years applied experience
• 3years of 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.

Preferred qualifications capabilities and skills

• 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.
• A portfolio showcasing successful applications of generative models in NLP projects including examples of utilizing OpenAI APIs for prompt engineering.