Top AI & Machine Learning Jobs

Provides enterprise technical leadership for modernizing Financial Crimes and Risk Management platforms. Designs scalable Java full-stack, cloud-native, distributed, Generative AI, and Agentic AI solutions. Advises executives, establishes architecture and engineering standards, drives resiliency, security, governance, reuse, and operational excellence, and influences cross-functional stakeholders. The role combines strategic architecture with hands-on prototyping, design validation, code review, technical problem-solving, mentoring, and evaluation of emerging technologies in a regulated banking environment.

Participate in a 12-month quantitative analytics program with two rotations focused on applied AI, machine learning, generative AI, agentic systems, and advanced modeling. Develop, fine-tune, evaluate, optimize, and deploy language models and scalable AI pipelines. Work with LLM agents, RAG, distributed training, model monitoring, and quantitative validation while collaborating with technical and business teams on financial-services challenges.

Ten-week Wells Fargo internship for master’s students applying quantitative analytics, machine learning, and generative AI to financial-services challenges. Interns may develop AI advisors, LLM assistants, agentic and multi-agent systems, RAG platforms, model-training pipelines, and scalable machine-learning solutions. Responsibilities include model evaluation, optimization, deployment, statistical validation, and risk-aware data analysis. The program includes mentorship, technical training, networking, and strategic project work in Charlotte, North Carolina.

Ten-week quantitative analytics internship focused on applying artificial intelligence, machine learning, generative AI, and statistical techniques to financial-services challenges. Interns may develop LLMs, RAG systems, multi-agent architectures, AI assistants, decision-support tools, and scalable machine-learning solutions. Responsibilities include model training, evaluation, optimization, deployment, quantitative validation, and collaboration with business and technical teams. The role includes mentorship, training, networking, and potential consideration for full-time employment.

Principal Engineer responsible for architecting and scaling enterprise GenAI platforms integrated with Java-based banking systems. The role combines distributed systems engineering, Spring Boot microservices, LLM applications, RAG pipelines, agent orchestration, cloud-native services, APIs, observability, resilience, and AI governance. The engineer will influence enterprise architecture, lead technical design, partner with business leaders, mentor engineers, and deliver secure, compliant, production-grade AI capabilities for commercial and corporate banking.

Participate in a 12-month quantitative analytics development program focused on applied AI and machine learning. Build and deploy LLM-powered agents, multi-agent systems, RAG applications, model training pipelines, and enterprise decision-support solutions. Projects include model evaluation, optimization, responsible AI, distributed training, and production monitoring. Collaborate with technical and business teams across two six-month rotations, applying quantitative methods to improve customer experience, risk management, productivity, and operational efficiency.

Lead design and delivery of AI-driven front-line monitoring solutions using Python, SQL, and SAS. Develop anomaly detection, feature engineering, visualizations, and agentic LLM workflows to detect conduct and compliance risks. Provide technical ownership for scalable, explainable, auditable analytics, mentor junior staff, and drive SAS-to-Python and GCP-based modernization.

Validate NLP and Generative AI models within Model Risk Management, assessing methodology, data integrity, performance, implementation, and monitoring. Develop test models, analytical reports, validation tools, and evaluation pipelines while managing model risk across the lifecycle. Collaborate with auditors, regulators, model stakeholders, and technology partners on quantitative modeling, governance, automation, and compliance initiatives.

Develop and deploy foundation models, generative AI, agentic systems, and traditional machine learning solutions at enterprise scale. Design multi-agent workflows, RAG applications, training and deployment pipelines, and human-in-the-loop systems. Pre-train, fine-tune, evaluate, monitor, and optimize transformer models using distributed computing and techniques such as distillation, quantization, pruning, LoRA, and PEFT. Collaborate with technical, business, regulatory, and audit stakeholders while supporting responsible AI, governance, risk management, and model performance improvement.

Manage and mentor quantitative analysts and credit risk analysts while leading hands-on development of foundation models, generative AI, agentic systems, LLM applications, and RAG solutions. Design, train, fine-tune, evaluate, deploy, monitor, and optimize transformer-based models and scalable AI infrastructure. Partner with business, technology, audit, and regulatory stakeholders on governance, risk management, model performance, and enterprise AI strategy.