Principal Architect

You are a trailblazer, leading the way in a specific area of architecture and making a profound impact on the people, technologies, and projects across departments. With your impressive experience steering multiple complex projects forward, the entire firm will reach new levels of success.

As a Principal Architect at JPMorgan Chase within the Consumer and Community Banking division, you lead innovation for AIML platforms, Generative AI platform and other Enterprise Data platform specific architecture and drive impact within teams, technologies, and projects across departments. Utilize your in-depth knowledge of ML tools, experiment and build state of the art ML as well as GenAI Platforms. You will be part of an innovative team, working closely with Product Owners, ML Engineering and Software engineering to build new AI/ML solutions and help deliver and evangelize end-to-end architecture. You will also take mentor other ML engineers and scientists while fostering a culture of constant learning and Architecture evolution at the firm. We are looking for someone with a passion for data, ML, and scalable platforms, who can build ML solutions at-scale with a hands-on approach with detailed technical acumen.

Job responsibilities

• Owns and evangelizes solutions related to data architecture, ML Platform as well as GenAI platform architecture, provide tactical solution and design support to the team and embedded with engineering on the execution and implementation of processes and procedures
• Owns governance accountability for coding decisions, control obligations, and measures of success such as cost of ownership, maintainability, and portfolio operations
• Serve as a subject matter expert on a wide range of ML techniques and optimizations.
• Provide in-depth knowledge of distributed ML platform deployment including training and serving.
• Create curative solutions using GenAI workflows through advanced proficiency in large language models (LLMs) and related techniques.
• Gain Experience with creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines.
• Get Hands on code and design to bring the experimental results into production solutions by collaborating with engineering team.
• Owning end to end code development in python/Java for both proof of concept/experimentation and production-ready solutions.
• Optimizing system accuracy and performance by identifying and resolving inefficiencies and bottlenecks. Collaborates with product and engineering teams to deliver tailored, science and technology-driven solutions.
• Drives decisions that influence the product design, application functionality, and technical operations and processes.

Required qualifications, capabilities, and skills

• Formal training or certification in software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
• At least 10 years experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.
• Solid understanding of using ML techniques specially in Natural Language Processing (NLP) and Large Language Models (LLMs)
• Hands-on experience with machine learning and deep learning methods.
• Good understanding in deep learning frameworks such as PyTorch or TensorFlow.
• Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search).
• Ability to work on system design from ideation through completion with limited supervision.
• Passion for detail and follow through. Excellent communication skills and team player.
• Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.
• Practical cloud native experience such as AWS.

Preferred qualifications, capabilities, and skills

• MS and/or PhD in Computer Science, Machine Learning, or a related field
• Experience with Ray, MLFlow, and/or other distributed training frameworks.
• In-depth understanding of Embedding based Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
• Advanced knowledge in Reinforcement Learning or Meta Learning.
• Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc.