Top Artificial Intelligence Jobs

Lead enterprise AI strategy and execution to design, pilot, deploy, and scale generative AI, conversational assistants, agentic automation, and AI-enabled workflows across clinical, product, and enterprise systems in a regulated healthcare environment. Align cross-functional teams, define success metrics, and ensure compliance, auditability, and continuous improvement of production AI capabilities.

Ship full‑stack features end-to-end using AI coding agents, drive architecture and technical direction, collaborate with Product and Design, write scalable maintainable code, mentor engineers, document work, and translate industrial automation customer problems into engineering tasks.

As a Senior AI Engineer, you'll develop ML and Generative AI models, ensure integration with enterprise applications, and manage large datasets while collaborating with teams and mentoring others.

The Director of AI Customer Success will advise enterprise clients, ensuring successful delivery and adoption of AI solutions while driving retention and value realization. Responsibilities include customer strategy, developing success metrics, managing relationships, and collaborating with internal teams.

Lead enterprise deployment, governance, and operations for Microsoft Copilot and Power Platform across M365. Manage tenant controls, licensing/credit governance, AI risk/compliance, Microsoft vendor relationships, SharePoint/OneDrive content readiness, and analytics/reporting. Partner with architects, Security, Legal, Procurement, and business stakeholders to ensure secure, compliant, and adopted AI platform operations.

Lead design and delivery of enterprise BI for Finance: architect Power BI solutions, build data models and dashboards, develop complex SQL transformations, ensure performance and governance, align stakeholders, and mentor teammates.

Lead design and delivery of a scalable AI platform for Aladdin: build Kubernetes-based model deployment, cloud-native infrastructure, CI/CD for ML, ensure security/compliance, troubleshoot platform reliability, translate business needs into robust technical designs, and collaborate across global teams.

As a Research Product Manager, you will oversee complex research programs, turning technical ideas into execution plans and aligning research with production systems to enhance AI capabilities.

Design, build, and deploy transformer-based ML and LLM systems to automate risk investigations and decisioning. Own end-to-end pipelines (data ingestion, prompt tuning, orchestration, productionization, monitoring) and collaborate with product, engineering, and operations to deliver copilot/autopilot workflows that improve analyst productivity and eliminate manual reviews.

The Applied AI Engineer will build and maintain AI agents, manage integrations and monitor performance, ensuring quality and continuous improvement of AI tools at Zello.

Responsible for leading design projects, translating AI insights into intuitive interfaces, conducting user research, and collaborating with cross-functional teams to deliver innovative health-focused designs.

The role involves designing and operating AI systems, building data pipelines, fine-tuning models, and mentoring team members while collaborating with product and data science teams.

Build a cross-platform endpoint agent to detect and attribute AI-generated code at the line level, design pipelines to capture/classify/store developer and agent activity, work with Git internals, integrate with coding agents, and ensure reliable, invisible production deployments across macOS, Linux, and Windows.

Lead enterprise AI consumption finance: define telemetry, unit-economics, allocation policies, vendor normalization, ROI and benefits realization, credit ledger governance, reporting, and cross-functional FinOps-for-AI governance with Finance, Procurement, and AI Operations.

The AI Operations Lead will identify operational bottlenecks, build AI automations and workflows, and drive cross-team collaboration to enhance efficiency and establish internal AI foundations.

Own and drive AI initiatives end-to-end: plan and track milestones, manage dependencies, remove risks and blockers, coordinate vendors and resources, facilitate cross-functional decisions (Medical, Legal, Regulatory, Compliance, Privacy), run governance and reviews, and produce clear status and reporting to leadership while promoting AI-enabled operating models.

The role involves researching, designing, and prototyping generative AI solutions for cybersecurity. Responsibilities include building datasets, training models, and presenting findings to various audiences.

Design, build, and productionize scalable agentic AI systems (agent orchestration, sandboxed execution, memory, latency optimization, knowledge graphs). Implement frontier AI architectures, collaborate with ML/security/product teams, mentor engineers, and deliver production-grade code and product improvements.

Lead investments to optimize database operation, understand customer issues, collaborate across teams, and advance database monitoring capabilities.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

As a Software Engineer Lead, you will design and develop AI solutions, lead tech teams, and ensure high-quality NLP capabilities. You'll integrate AI services into enterprise platforms and guide best coding practices.

As a Knowledge Development Engineer, you'll architect and deploy AI-driven production systems, reverse-engineer existing systems, and guide product development. You'll synthesize customer feedback and lead experiments to connect technical development with business outcomes.

The Knowledge Development Engineer leverages AI agents to solve customer problems by building applications and automating workflows, focusing on delivery speed and quality.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead development of a Semantic Brain: map raw event logs to industry ontologies, build concept/identity-anchored graphs, design reward extraction and off-policy evaluation, deploy agentic LLM orchestration and scalable data pipelines to enable autonomous enterprise decisioning.