Top AI & Machine Learning Jobs

Leads IAM’s transition to AI-native operations by identifying opportunities, delivering applied AI initiatives, establishing governance and measurement practices, coaching practitioners, and advising executives. The role oversees multidisciplinary transformation efforts involving generative AI, agentic systems, workflow automation, human-agent interaction, adoption, risk management, and benefits realization across a complex, regulated organization.

Leads end-to-end delivery of AI/ML use cases, including problem framing, engineering design, model development, deployment, monitoring, and production operations. Builds agentic AI and LLM-powered products using prompt engineering, RAG, fine-tuning, and governance controls. Sets engineering quality standards, mentors engineers, partners with business stakeholders, identifies new AI opportunities, and ensures responsible AI, regulatory, privacy, and cross-border requirements are addressed.

Develop and deploy AI and machine learning solutions for workforce analytics. Conduct statistical analyses, experiments, and model evaluations; build scalable model factories and evaluation frameworks; apply generative AI and agentic development approaches; collaborate with business, product, and technology teams; communicate insights; and ensure compliance with risk, control, and governance requirements.

Leads fraud-risk analytics and strategy across the account lifecycle, analyzing fraud trends, account quality, performance metrics, profitability drivers, and operational processes. Directs complex projects from analysis through implementation and monitoring, identifies fraud risk drivers, and delivers recommendations to senior leadership. Collaborates cross-functionally, builds consensus, and communicates quantitative insights to executives and stakeholders using statistical methods, SAS, SQL, and spreadsheet tools.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.

Conduct end-to-end analytics and causal investigations to improve LLM and GenAI products, identify customer friction, optimize model performance, automate reporting, and deliver actionable insights. Develop SQL and Python solutions across AWS and on-premises environments, collaborate with business and technology teams, investigate data and production issues, and support agile product development. Apply regression, clustering, classification, controlled experiments, causal inference, NLP, and machine learning techniques.

Leads hands-on architecture, development, and production deployment of scalable AI, machine learning, and LLM solutions for asset and wealth management. Designs data-processing pipelines, model inference services, and cloud-based platforms; experiments with and productionizes ML models; and partners with technical, product, and business teams. The role also requires leadership in AI innovation, solution design, data management, scalability, security, and communication with senior stakeholders.

Lead the architecture, development, deployment, and operation of enterprise AI/ML solutions for wholesale payments. Build production systems involving NLP, document AI, OCR, LLMs, RAG, and fine-tuning on AWS. Establish scalable data pipelines, MLOps, model governance, monitoring, and responsible AI practices. Partner with senior stakeholders, define roadmaps and success metrics, improve engineering standards, conduct technical reviews, and mentor engineers.

Lead the design, development, and operation of scalable data pipelines, trusted data products, metrics, and feature-ready datasets. Establish data quality, governance, orchestration, testing, observability, and engineering standards using Python, PySpark, SQL, dbt, Airflow, and Databricks. Partner with analytics, product, and engineering teams to support AI adoption and productivity measurement, while mentoring a team of two and driving delivery through ambiguous, cross-functional initiatives.

Leads a global AI and data science organization focused on workforce analytics. Sets technical strategy, directs research from hypothesis through production, develops model and evaluation factories, advances responsible agentic AI, and translates models into business and employee insights. Partners with technology teams on scalable platforms, prioritizes investments, communicates with senior leaders, and ensures compliance in a regulated enterprise environment.