Top Artificial Intelligence Jobs

Manage and execute Responsible AI governance workflows, intake validation, backlog remediation, documentation QC, audit readiness, and reporting. Coordinate stakeholders, prioritize and escalate higher-risk AI cases, drive process improvements, and mentor junior team members while scaling governance operations.

Lead design, develop, deploy, and support large-scale AI systems (foundation model training, LLM inference, vector search, guardrails, observability). Partner cross-functionally to optimize performance, scalability, and cost, and shape long-term foundational AI technical vision and roadmap.

Lead design, build, deploy, and support GenAI platform services (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, collaborate cross-functionally, contribute to technical vision and roadmap.

Execute and maintain Responsible AI governance workflows: intake validation, QC of AI submissions, remediation tracking, stakeholder coordination, reporting, and operational documentation to ensure audit readiness and timely closure.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Build and productionize agentic workflows and RAG pipelines, integrate LLMs into the GTM stack, and develop high-performance backend services and ML pipelines. Connect quoting engine, Salesforce, and data lakes, solve synchronization and architectural challenges, and ensure observability, scalability, and reliability for sales automation tools.

Lead analytics engineering for Revenue, Marketing, and Finance by building scalable data solutions, data models, and dashboards; provide training and consultation, support ad hoc data needs, and translate technical findings into executive-level insights to improve operational performance.

Lead client engagements to assess business challenges, define enterprise data and AI strategies, develop roadmaps, guide solution design and delivery, provide technical leadership and mentoring, and support practice growth via presales and thought leadership.

Lead and execute AI integration across finance and legal, owning strategy, sequencing, and delivery. Build and lead embedded AI Fellows, productionalize LLM-driven tools, drive adoption, measure business impact, and coordinate with external partners to consolidate initiatives and ensure enterprise readiness.

Lead enterprise AI integration across Product Development and Product Excellence. Build unified data infrastructure, deploy AI tools, reform program management, scale AI-powered quality and consumer insights, and lead an embedded AI Fellows team to drive adoption, measurable outcomes, and organizational change across hardware and consumer product lifecycles.

Design, develop, and maintain Python applications and AI agents, integrate AI solutions with APIs and data sources, build Power BI dashboards, prepare and model data, collaborate with stakeholders, document systems, and troubleshoot and improve analytics and automation solutions.

Lead and coordinate a team building generative AI solutions (agents, digital assistants). Provide technical direction, manage resources and schedules, enforce engineering best practices, ensure product quality, and mentor team members while delivering enterprise-scale AI systems across cloud and edge environments.

Lead technical development and deployment of generative-AI and ML models for condition monitoring and predictive maintenance. Design GPU-accelerated anomaly detection and digital twins, optimize deep learning for NVIDIA GPUs, enable edge/Jetson deployments, integrate multi-modal telematics data, build RAG-enabled LLM diagnostics, and lead cross-functional projects and stakeholder communications.

Design and deliver AI-enhanced product features, collaborate with engineering and customers, lead cross-functional initiatives and product strategy, gather requirements into roadmaps, and build customer relationships to drive enterprise-scale value.

The AI Integrator role focuses on program coordination, facilitating communication on AI initiatives, and aiding in strategy development for GTE & GSE, enhancing collaboration and knowledge sharing across engineering teams.

Lead and grow a team of ML engineers building applications for a graphically intensive simulation platform. Manage stakeholders, translate customer needs into technical requirements, prioritize between off-the-shelf, open-source, and greenfield solutions, coordinate cross-functional delivery, and oversee the full ML lifecycle from data infrastructure to deployment. Foster team ways of working and performance.

Lead and scale AI platform and agent engineering teams to build AI infrastructure, agent runtimes, MLOps/LLMOps, prompt and skills lifecycle management, and forward-deployed engineering for healthcare customers. Drive platform reliability, security, observability, enterprise integrations, and operational excellence to deploy AI-powered solutions at scale.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

Lead the AI/ML Engineering team to transform healthcare claims processing, driving automation and operational intelligence across claims systems.

Own AI features end-to-end from ideation to production for partner brands. Build evaluation, regression, and monitoring systems; embed with customers; codify playbooks and reliability infrastructure to safely ship GenAI/agentic systems at scale in a regulated environment.

Lead the evolution of HR tech by integrating Workday with AI-driven solutions, developing automated workflows, and ensuring user experience across HR systems.

Lead end-to-end operational delivery for Toast Embedded Finance (banking, lending, payments, collections, leasing, spend). Own SLA and quality performance, build dashboards and capacity models, and partner with Product/R&D to remove manual work. Drive an AI transformation to implement LLM-based agentic workflows and automation, upskill teams, manage 30+ managers, and provide executive-level strategy to turn Operations into a growth engine.

Lead planning, deployment, tuning, and operational support of CrowdStrike Falcon's AI/ML detection and response capabilities. Implement and troubleshoot AI-driven detection models, automation playbooks, API integrations, and threat-hunting for customers. Provide training, mentorship, and customer-facing collateral while optimizing detection efficacy and integrating Falcon into customers' security workflows.

The Senior Security Engineer will assess application security for AI systems, focusing on prompt injection techniques, threat modeling, and client training on AI security topics.

Own and maintain Wasabi's end-to-end revenue forecast and master model across products, regions, sales pods, and cohorts. Build what-if scenarios, reconcile ARR/bookings/billings/GAAP revenue, produce regular review packages, and lead variance walks. Partner with FP&A, Accounting, Sales Ops, BI, and Data Science; design quota structures; support annual planning; and use AI and ML-driven inputs to automate workflows and improve forecast accuracy.