Applied AI ML Lead
On-site · Jersey City, New Jersey, United States
Job Summary
Lead a local applied ML team within JPMorgan Chase to deliver high-impact AI/ML solutions for financial services. Translate business requirements into machine learning specifications, milestones, and agile delivery plans; design experiments, implement and productionize scalable, trustworthy, and explainable models; refine model capabilities using PyTorch and scikit-learn, and apply DoWhy for causal inference. Leverage Hugging Face Transformers and LangChain to explore counterfactual reasoning in large language models, and build end-to-end model development and operations workflows for training, deployment, monitoring, and continuous improvement. Mentor teams, foster inclusive culture, contribute to firm-wide ML communities through publications, talks, patents, and knowledge sharing. Strong background in LLMs/NLP/knowledge graphs/RL or time-series, deep proficiency in Python, Spark, and ML frameworks; familiarity with AWS Sagemaker, EMR; proven leadership and ability to align cross-functional stakeholders.
Required Qualifications
- Masters with 7+ years experience or PhD with 3+ years of experience in Computer Science, Information Systems, Statistics, Mathematics, or equivalent experience
- Track record of managing AI/ML or software development teams
- Hands-on practitioner developing production AI/ML solutions
- Knowledge and experience in machine learning and artificial intelligence
- Expert in at least one of the following areas: Large Language Models, Natural Language Processing, Knowledge Graph, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis
- Good understanding of Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics
- Must have good knowledge on agentic patterns and relevant frameworks, such as LangChain, LangGraph, Auto-GPT etc.
- Strong understanding of AI implementation in software development and legacy code transformation
- Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, AI evaluation, RAG (Similarity Search)
- Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn
- Programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy etc
- Familiarity in AWS Cloud services such as EMR, Sagemaker etc.
- Strong people management and team-building skills
- Ability to inspire collaboration among teams composed of both technical and non-technical members
- Effective communication, solid negotiation skills, and strong leadership
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