Applied AI/ML Engineer

Applied AI/ML Engineer

Shape the future of intelligent products by building agent-driven systems that solve meaningful business problems at scale

As an Applied AI ML Associate Senior at JPMorgan Chase, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products using AI/ML technologies in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. You will need to leverage your strong knowledge of ML, NLP, Deep Learning, Knowledge Graphs, LLM, and experience in working with massive amounts of data to build systems that reach JP Morgan scale.

Job responsibilities

• Incorporate LLM models in business solutions. Build and enhance the AI processing pipeline to achieve higher throughput and accuracy by customizing for specific use cases by using tools like Langchain, few shot learning, Chain of thought and other prompt engineering techniques.
• Develop production-grade agentic workflows  using modern orchestration frameworks (e.g. LangGraph , Google ADK and other agent frameworks), with strong focus on evaluation , observability, resilience, and maintainability.
• Explore LLM models and evaluate model performance and accuracy. Improve the accuracy of the models by customizing for specific use cases.
• Implement Retrieval-Augmented Generation (RAG) methods to enhance the LLM's ability to retrieve and generate accurate answers from large datasets. Develop end-to-end ML pipelines necessary to transform existing applications and business processes into true AI systems.
• Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to define requirements and deliver high-quality solutions. 
• You will collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and/or cause and effect relations.
• Utilize Prompt Engineering techniques to fine-tune and optimize LLMs for specific use cases and improve response accuracy and relevance.

 

Required qualifications, capabilities, and skills

• Advanced Degree in field of Computer Science, Data Science or equivalent discipline
• 4+ years of working experience as a hands-on ML Engineer/Data Engineer/Data Scientist, with at least 6 years of industry experience
• Strong communication skills along with significant experience of managing stakeholder of diverse background
• Hands on expertise with Python, Fast API and DL 
• Experience in designing and building highly scalable distributed ML models in production.
• Experience with analytics (ex: SQL, Python, AWS suite)
• Experience with machine learning techniques and advanced analytics (e.g. regression, classification, clustering,  causal inference, mathematical optimization)

Preferred qualifications, capabilities, and skills

• Experience as a Senior Data Scientist , in driving projects end to end is preferred
• Experience in LLM, building RAG pipeline is preferred
• Experience in large scale Machine Learning system design is preferred
• Experience working with end-to-end pipelines consisting of Cloud services is preferred
• Experience with AWS ML ecosystem (i.e. Sagemaker, etc.) is good to have.
• Frameworks like TensorFlow/PyTorch over GPU is preferred, BERT, SBERT,etc
  

Experience Level

Senior Level