Top AI & Machine Learning Jobs

Lead a data science team delivering analytics, machine learning, and AI solutions for wealth management. Partner with product, strategy, and operations stakeholders to frame problems, define success metrics, analyze client and portfolio outcomes, operationalize experimentation, and communicate executive-ready recommendations. Coach data scientists, develop scalable analytical capabilities, and identify high-value AI and agentic use cases that improve decision quality, client outcomes, and operational efficiency.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

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.

Lead a team of ML engineers building and operating scalable risk features, data pipelines, supervised and unsupervised models, LLM solutions, and explainability systems for financial crime monitoring. Responsibilities include productionizing ML applications, monitoring drift and bias, optimizing performance, developing Databricks pipelines, and collaborating with product, SRE, compliance, and operational teams.

Leads product analytics for small business lending by framing business questions, building and validating datasets, developing KPI and reporting frameworks, conducting statistical and predictive analyses, and translating findings into executive-ready recommendations. Partners with product, lending strategy, risk, finance, technology, and operations stakeholders to improve measurement, automation, data governance, customer understanding, and lending-product performance.

Leads a data science and AI/ML team supporting risk management and compliance. Oversees predictive models, LLM solutions, autonomous agents, and enterprise AI applications from design through deployment and monitoring. Partners with Product, Business, and Engineering teams to embed AI into workflows, improve operational efficiency, and deliver business value. Ensures model risk management, responsible AI, explainability, fairness, governance, drift monitoring, and retraining. Mentors teams, communicates insights to senior leadership, and guides research, experimentation, and innovation.

Leads development of secure, scalable cloud infrastructure for AI and machine learning workloads. Designs and operates Kubernetes-based inference platforms, builds CI/CD and infrastructure-as-code automation, optimizes cloud performance and costs, and partners with AI teams on compute requirements. Establishes responsible AI-assisted engineering practices, governance, validation standards, and reusable SDLC automation. Provides technical guidance, reviews production code, troubleshoots systems, and promotes operational stability across teams.

Leads data science and machine learning initiatives addressing cybersecurity and technology risk challenges. Responsibilities include exploratory data analysis, statistical and deep learning model development, feature engineering, secure production coding, model governance, AI workflow guardrails, and stakeholder communication. The role also supports security control effectiveness, production model monitoring, and responsible deployment of AI solutions using enterprise-authorized capabilities.

Design conversational flows for AI-driven chat and voice assistants, collaborating with machine learning, engineering, product, and UX teams. Use NLU and LLM expertise to refine models, develop prompts, shape conversational AI strategy, and create inclusive, accessible experiences. Present design rationale through storytelling and documentation, analyze user and market data, and iteratively improve products using feedback and insights.

Leads the design, development, and productionization of Generative AI and Agentic AI solutions. Builds LLM applications, RAG systems, tool-using agents, data pipelines, APIs, and cloud-native services using Python or Java on AWS. Establishes prompt engineering, evaluation, MLOps, testing, monitoring, security, and reliability practices while collaborating with stakeholders on requirements, metrics, and deployment strategies.