Top AI & Machine Learning Jobs

Design, develop, and maintain cloud-native AWS applications using Java and React. Leverage enterprise AI-assisted development and LLM-driven solutions to produce secure, high-quality code, improve automation and operational stability, troubleshoot issues, and contribute to system architecture and engineering best practices.

Build secure, scalable full-stack and cloud-native applications using Python, AI/ML, LLMs, generative AI, and IoT integrations. Responsibilities include designing architecture, developing backend and UI features, integrating connected-device data, analyzing datasets, troubleshooting production systems, and improving reliability. The role also requires delivering smart-building and workplace solutions using distributed systems, cloud platforms, microservices, and edge-computing patterns.

Leads the analytics and artificial intelligence strategy for Auto Commercial Solutions, overseeing data scientists and quantitative analysts. The role develops machine learning, predictive analytics, generative AI, experimentation, governance, and scalable data products to improve dealer acquisition, relationship growth, sales effectiveness, portfolio performance, and commercial lending opportunities. It partners with senior leaders across product, technology, risk, finance, and sales while translating complex insights into business recommendations and building a high-performing analytics organization.

Leads a strategic insights team analyzing product development productivity, operating models, workflows, and AI adoption. Frames hypotheses, conducts quantitative analysis and experiments, evaluates delivery and business outcomes, and translates findings into executive recommendations. Partners with Product Operations, Technology, Product, and Finance to implement actions, track adoption, and measure results. Establishes analytical standards, communicates with senior leaders, and develops a high-performing analytics team.

Develop AI/ML proofs of concept and deployable models for compliance and operational risk analytics. Responsibilities include analyzing structured and unstructured data, designing methodologies, building scalable models and data pipelines, applying machine learning, graph learning, LLMs, and agentic solutions, and documenting quantitative models for governance and validation. The role collaborates with data scientists, technology teams, risk professionals, and model validation groups in a regulated financial-services environment.

Lead development of reusable AI platform capabilities, including APIs, libraries, reference architectures, and production services. Design secure, scalable, and resilient LLM-backed systems on AWS; establish engineering, testing, operational, and security standards; evaluate AI solution architectures; promote AI-assisted development practices; and mentor engineers across teams.

Leads software engineering and AI initiatives for enterprise-scale banking technology. Designs scalable frameworks, writes and reviews production code, and establishes secure, auditable agentic AI development workflows. Defines responsible AI guardrails, target-state architecture, reusable engineering practices, and cloud-native solutions. Advises cross-functional teams, influences senior stakeholders, serves as a technical subject matter expert, and collaborates on product and engineering roadmaps.

Lead data science initiatives for Risk Management and Compliance, translating business problems and KPIs into actionable insights. Develop, train, test, and deploy machine learning models for credit risk, including classical, deep learning, graph network, agentic AI, and large language model solutions. Build reliable AI agents, conduct rigorous testing, collaborate with senior stakeholders, and deliver scalable data extraction, modeling, visualization, and analytics solutions.

Leads software engineering for a Machine Learning I/O team, developing secure production code and guiding design, testing, debugging, and operational stability. Drives enterprise adoption and governance of AI-assisted development tools, establishes validation standards for AI outputs, promotes secure and responsible AI practices, and influences technical decisions across teams. Provides technical direction, serves as a subject matter expert, and advocates for scalable cloud-native engineering practices.

This 3–6 month quantitative trading and research internship develops mathematical models, pricing and hedging tools, risk models, algorithmic trading strategies, and portfolio optimization methods. Associates apply machine learning, advanced analytics, numerical algorithms, and high-performance computing to derivatives, electronic trading, market making, and risk management. The role involves research, scenario analysis, model validation, production implementation, and collaboration with traders, marketers, and risk managers.