Description When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this. As a Senior Lead Software Engineer at JPMorgan Chase within the ALML, Corporate Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. Job responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems 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-4), 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. Adds to team culture of diversity, equity, inclusion, and respect Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and proficient in applied experience 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. Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security 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. Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) 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.