JPMorgan Applied AI ML Lead 

India, Karnataka, Bengaluru 

179989261

Job responsibilities

• Establishes and promotes a library of common ML assets, including reusable ML models, features stores, data pipelines, and standardized templates.

• Leads efforts to create shared tools and platforms that streamline the end-to-end ML lifecycle across the organization.

• Creates curative solutions using GenAI workflows through advanced proficiency in large language models (LLMs) and related techniques.

• Gains Experience with creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines

• Advises on the strategy and development of multiple products, applications, and technologies.

• Leads advisor on the technical feasibility and business need for AIML use cases.

• Liaises with firm wide AI ML stakeholders.

• Translates highly complex technical issues, trends, and approaches leadership to drive the firm's innovation and enable leaders to make strategic, well informed decisions about technology advancements.

• Influences across business, product and technology teams and successfully manages senior stakeholder relationships.

• Champions the firm's culture of diversity, opportunity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Formal training or certification on Machine Learning concepts and 5+ years applied experience.
• MS and/or PhD in Computer Science, Machine Learning, or a related field and practical cloud native experience such as AWS needed.
• 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.
• 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 PyTorch or TensorFlow.
• Experience in advanced applied ML areas such as GPU optimization, fine-tuning, 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.
  

• 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.