Top Artificial Intelligence Jobs

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.

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.

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.

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.

As an AI Engagement Manager, you will ensure customer success through project management, stakeholder engagement, and data-driven adoption strategies in a fast-paced SaaS environment.

Build, refine, and scale production agent systems: multi-step tool-using agents, memory systems, proactive agents, and evaluation frameworks. Ensure fast, predictable, traceable behavior and uptime while working across infra, frontend, and product.

The AI/ML Software Developer will design, develop, and maintain AI applications, collaborate with teams, solve problems, and manage software debugging and documentation tasks.

Lead marketing data and AI strategy: architect the single source of truth, design scalable data models, create measurement and attribution frameworks, prioritize and productionize predictive AI use cases, and embed responsible AI governance across marketing.

This role involves leading delivery pods, managing the product backlog, partnering with product teams, and embedding AI capabilities into LaserPro.

Build and ship agent-based growth orchestration: implement agent workflows, integrate AI reasoning with growth platforms and data systems, create reusable agent infrastructure and creative pipelines, and ensure monitoring, auditability, and reliable integrations across growth channels.

Lead enterprise AI adoption by assessing processes, identifying AI use cases, developing implementation playbooks, and running workshops. Partner with IT, Security, Compliance, HR, and business units to design AI-enabled workflows, ensure governance and regulatory alignment, deliver role-based AI education, and measure adoption and productivity gains while preserving human oversight and organizational knowledge.

Define and guide architecture for AI/ML on edge devices: reference architectures, data pipelines, inference, deployment, security, and cross-team enablement to deliver real-time industrial insights and scalable edge solutions.

The AI Engineer will design and build AI systems using LLMs, develop agent workflows, implement APIs, and collaborate with teams to optimize AI applications.

Design and lead end-to-end GenAI solution architectures for enterprise customers, run technical discovery and proof-of-value pilots, engage senior stakeholders in pre-sales, collaborate with engineering and customer success for rollout, and create reusable ontologies, reference architectures, and field-driven product feedback.

Design and implement systems software for high-performance GPU clusters. Debug complex distributed systems and own the systems lifecycle from design to production.

Lead development and deployment of AI-first systems: build generative and discriminative models, enable multimodal behavior classification from mobile signals, define prompting and benchmarking standards, own ML tooling and SDLC, and partner with Product and Engineering on the AI roadmap.