This listing doesn't post a salary. Most similar roles pay
$162,000–$246,741.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As an Applied AI/ML Senior Associate within the Risk Technology team, you will join an agile team to design and deliver secure, stable, and scalable technology products. You will be responsible for developing high-quality production code, creating architecture artifacts, and troubleshooting complex technical problems across various business functions. Your daily work involves managing the full machine learning lifecycle—from prototyping to deployment—while analyzing large datasets to improve system architecture. The role requires expertise in Python, Flask or FastAPI, and the ML/NLP ecosystem including scikit-learn, pandas, NumPy, spaCy, PyTorch, and TensorFlow. You will utilize Spark, Databricks, SQL, and LLM patterns like RAG and agentic AI frameworks. Additionally, you will address technical challenges within a regulated financial-services environment, focusing on risk domain concepts such as market, credit, counterparty, and investment risk.
Formal training or certification in software engineering and 5+ years of experience building production Python systems including web/API services and the ML/NLP ecosystem.
Demonstrated experience taking machine learning models from prototype to production across all stages of deployment and monitoring.
Proven experience with large datasets, distributed compute (Spark/Databricks), and SQL fluency regarding partitioning and performance.
Hands-on experience in system design, application development, testing, and maintaining operational stability for daily production services.
Experience developing and debugging code in a large corporate environment using version control and modern programming languages.
Working knowledge of LLM application patterns including RAG, prompt design, embeddings, vector search, and tool calling.
Experience with agentic AI frameworks, multi-agent orchestration, and integration patterns like Model Context Protocol (required).
Knowledge of SDLC, agile delivery practices, CI/CD, application resiliency, and security.
Production experience with Databricks, Apache Airflow, and various supervised/unsupervised machine learning techniques (preferred).
Solid grounding in data pre-processing, feature engineering, model selection, and hyper-parameter tuning (preferred).
Applied NLP experience, computer vision exposure, and familiarity with AWS machine learning services (preferred).
Knowledge of deep learning architectures, reinforcement learning, and building self-service ML tooling (preferred).
Experience with AI/ML observability and model governance in a regulated financial-services environment (preferred).
Machine Learning
Deep Learning
LLMs
Generative AI
NLP
Python
Hugging Face
Transformers
PyTorch
TensorFlow
NumPy
Scikit-Learn
Pandas
AWS
SageMaker
Prompt Engineering
Big Data
LLM
Natural Language Processing
Machine Learning
Gen AI
MLOps
AWS
GCP
Azure
Data Analysis
Graph Learning
Recommendation Systems
Reinforcement Learning
Data Structures
Algorithms