Top AI & Machine Learning Jobs

Build, validate, and deploy consumer credit risk and loss models and modernize model pipelines. Partner with cross-functional teams to implement machine learning solutions at scale using Python, Spark, H2O, and cloud platforms, and translate model results into business and regulatory insights for stress testing, capital allocation, and portfolio management.

The role involves leading AI product development, working with cross-functional teams to enhance AI systems, and optimizing large-scale production AI systems.

Owner of product features for Card Intelligence ML initiatives; collaborate with data scientists, engineers, and stakeholders to define roadmaps, break down epics into MVPs, prioritize features, and accelerate model-to-market delivery for fraud prevention.

The Applied Researcher I role involves developing AI foundation models, collaborating with cross-functional teams, and conducting advanced research to enhance banking experiences with AI.

Lead a team to analyze data, create machine learning models, and deliver insights for customer-focused financial products, leveraging various technologies.

The role involves building machine learning models, collaborating with teams, and leveraging technology to analyze data, ultimately enhancing credit underwriting for business cards.

Lead quantitative model risk assessments and develop alternative modeling approaches for loan loss forecasting and ACL. Apply econometric, statistical, and machine learning methods, maintain model accuracy, produce transparent documentation, and communicate technical results to management, regulators, and stakeholders. Leverage cloud and open-source tools, collaborate with model development and risk teams, and drive continuous improvement in modeling practices.

Lead design, development, deployment, and support of foundation model training and LLM inference platforms. Build similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, and scalability; contribute to technical vision and roadmap while partnering across engineering, research, product, and program teams.

Lead Machine Learning Engineer to design and productionize generative AI and agentic multi-agent systems. Architect and scale agentic platforms, integrate evaluation and observability pipelines, ensure data/privacy guardrails, deploy production LLM/GenAI solutions, optimize model orchestration and RAG patterns, and provide technical leadership and code/architecture reviews for engineering teams.

Lead development of next-generation entity resolution solutions using transformers, graph ML, deep learning, and big-data tooling. Partner with engineers and product to design, train, evaluate, validate, and deploy ML models at scale; leverage Agentic AI workflows; translate technical work into business impact across enterprise use cases.