Location: Bengaluru, Karnataka, India

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 firm’s 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
• 3+years 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.