Join our innovative team and shape the future of software development with AI-driven solutions.

As an Applied AI/GenAI ML Director within the Ai4Tect Team at JPMorgan Chase, you will utilize your profound engineering knowledge to collaborate with agile teams in enhancing, creating, and delivering trusted, top-tier technology products in a secure, stable, and scalable manner. Your deep expertise will be leveraged to consistently challenge the norm, innovate for business impact, and spearhead the strategic development of new and existing products and technology portfolios. You will stay abreast of industry trends, best practices, and technological advancements. Your role will concentrate on establishing and nurturing common capabilities, best practices, and reusable frameworks, thereby laying the groundwork for AI excellence that propels innovation and consistency across business functions.

Job responsibilities

• Establish and promote a library of common ML assets, including reusable ML models, features stores, data pipelines, and standardized templates.
• Lead efforts to create shared tools and platforms that streamline the end-to-end ML lifecycle across the organization.
• 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
• Advise on the strategy and development of multiple products, applications, and technologies.
• Lead advisor on the technical feasibility and business need for AIML use cases.
• Liaise with firm wide AI ML stakeholders.
• Translate highly complex technical issues, trends, and approaches to leadership to drive the firm's innovation and enable leaders to make strategic, well informed decisions about technology advancements.
• Influence across business, product and technology teams and successfully manages senior stakeholder relationships.
• champion the firm's culture of diversity, opportunity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Formal training or certification on Machine Learning concepts and 10+ years applied experience in one of the programming languages like Python, Java, C/C++, etc. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
• MS and/or PhD in Computer Science, Machine Learning, or a related field
• Solid understanding of using ML techniques specially in Natural Language Processing (NLP) and Large Language Models (LLMs)
• Strong understanding of AI implementation in software development and legacy code transformation.
• Solid understanding of Data Architecture and Engineering, building Data platforms/ Data Lakehouse
• Familiarity with agentic workflows and relevant frameworks, such as Lang Chain, Lang Graph,
• Get Hands on code and design to bring the experimental results into production solutions by collaborating with engineering team.
• Good understanding in deep learning frameworks such as Py Torch or Tensor Flow.
• 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 needed.

Preferred qualifications, capabilities, and skills

• Experience with Ray, MLFlow, and/or other distributed training frameworks.
• Understanding of Embedding based Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
  using 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.