Top AI & Machine Learning Jobs

Build and deploy AI-powered go-to-market agents, growth experiments, enrichment and signal-detection systems, lead scoring, outbound programs, attribution, SEO tooling, and sales enablement workflows. The role requires independently shipping experiments, measuring pipeline and conversion impact, contributing to Foley’s shared AI platform and ontology, and applying appropriate consent, privacy, deliverability, and release guardrails.

Leads the enterprise AI risk and governance function, establishing multi-year strategy, governance frameworks, risk standards, assessment processes, model risk management, monitoring, control testing, and regulatory readiness. Advises executives on AI adoption and risk decisions, partners with control and technology functions, oversees third-party AI requirements, represents the firm in examinations and audits, and builds a specialized AI risk organization.

Analyze large datasets to identify fraud patterns, emerging risks, and strategy improvements. Develop fraud detection rules and models, monitor performance and loss metrics, build dashboards and reporting, evaluate data sources and analytical methods, and translate findings into recommendations. The role partners with Fraud, Risk, Product, Technology, and Operations teams while applying statistical analysis, predictive modeling, AI tools, and sound judgment when handling sensitive data.

Build and operate production machine learning and generative AI infrastructure, including Azure and Databricks pipelines, CI/CD workflows, RAG systems, APIs, model-serving services, infrastructure as code, automated testing, monitoring, versioning, and governance. The role requires collaboration with AI, ML, data, infrastructure, security, and governance teams to deliver scalable, secure, reproducible, and high-performance AI solutions.

Build and debug LLM agents, integrations, and AI workflows using Python and APIs. Provide technical office hours, mentoring, code reviews, and debugging support to colleagues. Develop reusable agent frameworks, starter kits, evaluations, documentation, and learning resources. Partner with internal teams on AI platform adoption, identify skill gaps, track learning outcomes, and escalate complex data, security, or production issues.

Leads AF Group’s AI and emerging technology strategy, translating business priorities into prioritized use cases, business cases, roadmaps, and measurable outcomes. Directs cross-functional delivery from experimentation through production adoption, coordinating security, platform, application, data, and Center of Excellence partners. Evaluates generative AI, machine learning, and automation technologies; manages budgets, resources, change management, operational readiness, adoption, and value realization. Supervises employees and develops organizational AI capabilities.

Build statistical and machine learning models using healthcare claims, financial, organizational, and public data. Develop healthcare metrics, clinical ontologies, entity-resolution models, affiliation networks, organizational maps, sourcing methodologies, and account-targeting signals. Partner with Product, Data Engineering, Engineering, and QA to move solutions from prototype to production. The role requires substantial healthcare data expertise, comfort with ambiguity, and the ability to communicate technical concepts clearly.

Develop AI-agent capabilities for the MOOSE multiphysics simulation ecosystem, including automated model generation, simulation execution, error diagnosis, and verification. Integrate simulation tools and scientific domain knowledge, build agentic workflows, and evaluate performance on representative multiphysics problems. The intern will collaborate with computational scientists in an onsite research environment and may receive course credit.

Leads a portfolio of biomedical AI research, develops multimodal models integrating imaging, EHR, wearable, and genetic data, and guides ML engineering and data science teams. Responsibilities include securing research funding, building collaborations, generating proof-of-concept results, fine-tuning foundation models and LLMs, evaluating models rigorously, mentoring technical teams, publishing research, and communicating results to federal sponsors and partners.

Analyzes large datasets to develop solutions for credit risk, fraud analytics, customer segmentation, portfolio management, and forecasting. Responsibilities include extracting and validating data, building models, dashboards, reports, and automated workflows, supporting scorecard monitoring and stress testing, documenting requirements, identifying controls, and presenting insights to stakeholders. The role requires collaboration, strong communication, statistical expertise, and proficiency with multiple programming languages, databases, visualization tools, and machine learning techniques.