Top Artificial Intelligence Jobs

Owns execution and operationalization of enterprise AI strategy into scalable solutions. Leads portfolio delivery, stakeholder alignment, governance, responsible AI practices, adoption/change, and cross-functional teams to drive measurable business value.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Lead modernization of month-end and year-end close processes using AI and automation. Prepare, review, and validate journal entries, accruals, and financial statements. Implement anomaly detection, automated reconciliations, variance analysis, and predictive insights. Collaborate with data teams to structure finance data for ML use cases and design audit-ready, scalable reporting workflows.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Lead end-to-end delivery of enterprise AI/ML and GenAI programs, managing POC-to-production deployments, global engineering and data teams, stakeholder governance, KPIs, budgets, and timely, cost-effective delivery.

Design realistic, specialty-specific clinical case scenarios and gold-standard attending-level responses, evaluate and grade AI-generated outputs using structured rubrics, provide detailed written feedback to improve model clinical reasoning, and participate in onboarding, calibration, and review sessions in a fully remote, asynchronous contractor role (~20 hours/week).

Lead end-to-end delivery of enterprise AI/ML and GenAI programs from POC to production. Manage global engineering and data teams, LLM applications (RAG, agents, copilots), data science deployments, and AI-driven automation. Drive program governance, KPIs, budgets, stakeholder engagement, and ensure timely, cost-efficient, high-quality delivery.

PhD-level physicists will design challenging graduate-level physics problems and contribute high-quality datasets to improve AI model reasoning, ensuring scientific rigor and consistency while collaborating remotely and asynchronously.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Manage and optimize end-to-end account reconciliations, intercompany eliminations, and chart-of-accounts governance. Identify discrepancies, perform variance analysis, and implement AI-driven automation and anomaly detection to reduce manual effort and accelerate close. Collaborate with cross-functional teams to resolve mismatches and improve financial data quality and reporting accuracy.

Lead AI-enabled financial planning and strategic analysis, build data-backed business cases, refine unit economics, deliver executive-level insights, integrate ML into FP&A, and track KPIs to drive profitability and strategic decisions.

Design and implement AI/NLP solutions to automate insurance policy administration (issuance, endorsements, renewals). Translate policy language into models and rule engines, integrate with PAS, ensure regulatory compliance, monitor model performance, and support underwriting, operations, and exception handling.

Build and maintain operating and cash-flow models, apply AI/ML and data techniques to automate forecasts, run sensitivity and scenario analysis, translate strategy into financial outcomes, support fundraising/M&A/pricing, and create dashboards and reports for stakeholders.

Lead integration of AI-driven tools into tax provision, planning, and multi-jurisdictional compliance. Design ML/analytics solutions for sales and payroll tax, enhance regulatory filings, support audits, and collaborate with finance, legal, and technology teams. Monitor tax regulation changes, document AI tax models for transparency and governance, and continuously refine models for scalability, reliability, and compliance.

Design, build, and optimize Azure-based, cloud-native .NET applications and services. Implement serverless, event-driven architectures, CI/CD, TDD, and AI-augmented development. Mentor engineers, participate in architecture and code reviews, and collaborate across Product, QA, and DevOps to deliver secure, scalable healthcare solutions.

Lead strategy and execution for Arizona law firm clients, building GEO/AEO programs, running technical and content SEO audits, producing practice-area content compliant with ethics rules, reporting on signed cases and acquisition metrics, and supporting new business and cross-team delivery.

The Senior AI/ML Engineer will design, develop, and deploy language models for African languages, focusing on NLP applications and cultural context.

Lead architecture and integration of RISC-V-based AI compute clusters and vector engines. Define ISA extensions, memory subsystems, interconnects, task-mapping and scheduling, and performance/debug features. Collaborate with compiler, ML runtime, RTL, verification, and firmware teams to optimize performance-per-watt and scalability across silicon generations.

Design, build, and deploy production-grade AI applications on the Snowflake AI Data Cloud, including LLM-based copilots, RAG/semantic search, agentic workflows, and enterprise knowledge systems. Conduct data exploration, build vector retrieval and embeddings pipelines, implement evaluation and responsible AI practices, and deliver scalable, governed AI services integrated with enterprise systems. Partner with business stakeholders to translate problems into production AI solutions and establish platform standards and reusable components.

Verify chip-level and performance behavior of custom ASICs: design end-to-end test flows with architecture team, run simulation-based verification, implement UVM environments, use constrained-random and functional-coverage methodologies, and coordinate vendors/contractors.

The role involves high-volume outbound and inbound calls to qualify and capture information from business owners seeking insurance solutions, focusing on speed and accuracy while setting opportunities for the sales team.

Manage and scale enterprise AI initiatives by owning use-case intake, coordinating pilots, defining success criteria, developing playbooks, synthesizing adoption insights, and supporting governance and issue resolution across cross-functional teams.