Top AI & Machine Learning Jobs

Embeds with Markets, Banking, and Payments teams to identify high-value generative AI opportunities. Translates business needs into technical requirements, reusable components, and production-grade solutions. Owns architecture and development across data, retrieval, prompting, orchestration, evaluation, deployment, and iteration. Partners with Technology and data science teams to meet enterprise resilience, controls, and risk standards while delivering measurable commercial impact.

Leads end-to-end development and production deployment of machine learning, generative AI, and agentic solutions for payments and banking. Responsibilities include problem framing, experimentation, evaluation, MLOps, LLMOps, governance, monitoring, scalable cloud architecture, security, and systems integration. The role establishes reusable AI capabilities, partners with cross-functional stakeholders, mentors technical teams, and communicates model outcomes and operational tradeoffs to technical and non-technical audiences.

Leads a global data science organization delivering AI and machine learning solutions for workforce decision-making. Sets technical strategy, directs research from hypothesis through production, develops model and evaluation factories, advances responsible agentic engineering, and partners with technology teams on scalable enterprise data platforms. Aligns resources to measurable business outcomes, communicates with executives, fosters reuse and partnerships, and ensures compliance with organizational controls.

Leads full-stack software engineering and infrastructure platform development for AI-driven, self-healing data center operations. Designs Java, Python, and Node.js applications, scalable APIs, CI/CD automation, cloud-native platforms, VMware integrations, and infrastructure-as-code workflows. Develops machine learning models for forecasting, anomaly detection, and test classification, integrating them into operational systems for predictive alerts and workload orchestration. Provides technical leadership, mentors engineers, reviews code, and drives firm-wide architecture and technology decisions.

Summer analysts will build and deploy machine learning, generative AI, and agent-based solutions; integrate complex datasets; develop predictive models; run experiments; analyze trends; and deliver actionable insights. They will also support data governance, risk management, data quality, privacy, and regulatory controls while collaborating with agile, cross-functional teams. The program includes mentorship, training, and potential full-time opportunities after graduation.

Design and deploy machine learning systems for data analysis, document management, client intelligence, NLP, speech recognition, forecasting, reinforcement learning, and recommendations. Build scalable ML frameworks, conduct experiments, evaluate and monitor models, perform A/B testing, and deploy production solutions across cloud platforms. Develop CI/CD pipelines, containerized services, automated hyperparameter searches, testing frameworks, and data quality metrics while collaborating with partner teams.

Lead the architecture and delivery of scalable, secure cloud-native platforms across public clouds. Define reference architectures, engineering standards, governance controls, observability practices, and reusable developer-platform capabilities. Drive responsible adoption of generative and agentic AI in engineering workflows, including validation, auditability, security, and data protection guardrails. Partner with architecture, engineering, product, security, risk, and business teams while mentoring technical talent and setting enterprise architecture direction.

Develop, validate, onboard, and manage machine learning models for fraud and credit risk. Responsibilities include feature engineering, model training, performance monitoring, drift analysis, root-cause analysis, model governance, regulatory documentation, and collaboration with product, technology, legal, compliance, and external vendor teams.

Leads hands-on development of agentic AI solutions on NEO, including agent execution, sandboxing, integrations, memory, retrieval, evaluations, guardrails, and authorization. Builds full-stack TypeScript and Next.js applications, writes secure production code, improves engineering automation, and supports operational stability. Mentors engineers, promotes responsible AI-assisted development, establishes validation standards, and drives adoption of emerging technologies across Software Engineering.

Oversee AI risk assessments and governance for AI/ML solutions in client onboarding. Execute data-driven KYC control testing, identify operational and compliance gaps, document audit-ready results, and track remediation. Develop AI-enabled automation, prompt templates, and BAU monitoring solutions using large datasets and analytical tools. Partner with business, controls, operations, risk, and technology stakeholders to improve testing efficiency and proactively identify issues.