Applied AI ML Lead

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Commercial & Investment Bank

Lead an applied AI/ML team within a financial services context, focusing on developing and productionizing scalable, trustworthy, and explainable solutions. Responsibilities include defining technical vision, managing ML workflows, and applying expertise in areas like LLMs, NLP, RL, and advanced ML techniques including inference, fine-tuning, RAG, and prompt engineering. The role requires hands-on experience and leadership in building and operating AI/ML solutions.

What you'd actually do

  1. Lead a local applied machine learning team and collaborate across a global organization to deliver high‑impact outcomes.
  2. Define technical vision, shape strategic roadmaps, and align stakeholders across product, business, and technology.
  3. Translate business requirements into machine learning specifications, milestones, and agile delivery plans.
  4. Design experiments, implement algorithms, validate results, and productionize scalable, trustworthy, and explainable solutions.
  5. Build and operate model development and operations workflows for training, deployment, monitoring, and continuous improvement.

Skills

Required

  • 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.
  • Experience as a 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

Nice to have

  • Familiarity in AWS Cloud services such as EMR, Sagemaker etc.
  • Strong people management and team-building skills.
  • Ability to coach and grow talent, foster a healthy engineering culture, and attract/retain talent.
  • Ability to build a diverse, inclusive, and high-performing team.
  • Ability to inspire collaboration among teams composed of both technical and non-technical members.
  • Effective communication, solid negotiation skills, and strong leadership.

What the JD emphasized

  • Track record of managing AI/ML or software development teams.
  • Experience as a hands-on practitioner developing production AI/ML solutions.
  • Must have good knowledge on agentic patterns and relevant frameworks, such as LangChain, LangGraph, Auto-GPT etc.
  • Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, AI evaluation, RAG (Similarity Search).

Other signals

  • Lead applied machine learning team
  • Define technical vision, shape strategic roadmaps
  • Design experiments, implement algorithms, validate results, and productionize scalable, trustworthy, and explainable solutions
  • Build and operate model development and operations workflows for training, deployment, monitoring, and continuous improvement
  • 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
  • Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, AI evaluation, RAG (Similarity Search)