Top Artificial Intelligence Jobs

Leads and mentors an SDET team while remaining hands-on with test frameworks, automation, tooling, debugging, and code reviews. Owns functional, performance, contract, and chaos testing strategy for large-scale distributed AI security services. Establishes CI/CD quality gates, improves observability and resilience, partners with engineering and product teams, manages planning and delivery, and recruits and develops engineering talent. The role also uses AI-powered tools to improve testing efficiency.

Leads Operations issue testing, including planning, validation execution, resource leadership, and review of final results. Assesses remediation effectiveness, issue root causes, risks, control design, and operating effectiveness. Develops testing strategies, produces audit-defensible documentation, challenges stakeholders, and matures the issue validation program. Requires strong risk, controls, communication, project management, and leadership skills in banking or another regulated industry.

Lead Clio Operate implementations for mid-sized and large law firms from kickoff through support handoff. Manage requirements workshops, sprints, testing, change management, budgets, profitability, resources, timelines, risks, and client relationships. Partner with internal teams to promote product best practices, ensure successful adoption, and improve service delivery. The role requires Agile project management, SaaS architecture knowledge, strong communication, and cross-functional stakeholder leadership.

Provide legal counsel across product lifecycle for AI agents and SaaS products, triage and resolve legal issues, advise Product/Design/Engineering/Marketing, manage IP, monitor AI/privacy regulations, build scalable compliance processes, and deliver training.

Designs and ships production multi-agent AI systems for KYB, underwriting, case review, and risk monitoring. Owns agent architecture, retrieval, tool integrations, evaluations, observability, MLOps, reliability, and compliance guardrails. Requires strong Python engineering, agent framework expertise, distributed systems experience, Kubernetes operations, and production LLM deployment skills. Mentors engineers and partners with product, security, compliance, and applied science teams. Remote employees travel to Orlando at least twice annually.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Designs, develops, deploys, and optimizes production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role establishes AI cost-performance standards, leads technical design reviews, contributes to platform roadmaps, optimizes model and hardware utilization, and mentors senior AI engineers while delivering scalable and responsible AI capabilities.

Designs, develops, deploys, and supports production AI systems, including foundation models, LLM inference, agentic workflows, guardrails, evaluation, and observability. Owns complex AI architecture, reliability objectives, governance, and optimization for cost, latency, throughput, and hardware utilization. Partners across engineering, research, product, and program teams; leads technical reviews; contributes to long-term platform strategy; and mentors senior technical associates.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, and multi-model orchestration. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads governance reviews and design councils, contributes to technical roadmaps, establishes responsible AI standards, and mentors senior engineering leaders.

Lead the engineering organization responsible for large-scale generative AI training infrastructure, including distributed training, reinforcement learning, GPU scheduling, fault tolerance, model experimentation, and cost governance. Define AI platform strategy, responsible AI standards, observability, governance, and long-term roadmaps. Partner with research, product, compliance, and risk teams while hiring, mentoring, and developing technical talent across multiple teams.

Own intake and prioritization of early-stage Card GenAI use cases. Collaborate with designers, engineers, data scientists, and stakeholders to define product opportunities, acceptance criteria, user stories, MVPs, roadmaps, and feature priorities. Apply hypothesis-driven analysis, agile delivery, customer research, and technology strategy to develop digital products while managing risk and communicating business value.

Leads enterprise AI enablement product strategy and development, including internal AI infrastructure, autonomous agents, RAG systems, semantic layers, integration capabilities, governance, evaluation pipelines, and responsible AI guardrails. Drives workflow transformation, adoption, change management, cross-functional alignment, and feedback loops across engineering, finance, legal, and business teams.

Develop and operate large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, guardrails, model evaluation, vector search, memory, governance, and observability. Lead architecture and optimization for cost, latency, throughput, reliability, and hardware utilization. Define AI service objectives, ensure security and compliance, guide technical roadmaps, and mentor engineering teams.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Lead technical reviews and governance, define AI platform roadmaps, integrate heterogeneous AI systems, and mentor senior engineering talent.

Design, develop, deploy, and support large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Establish governance and engineering standards, lead design reviews, contribute to platform strategy, and mentor senior AI engineers.

Provides executive-level legal technology advisory services to General Counsel, CLOs, and legal operations leaders. Advises customers on contract transformation, AI-driven contract intelligence, governance, compliance, risk management, and legal innovation. Supports enterprise sales opportunities, develops ROI and business cases, contributes to solution design and RFPs, influences product strategy, and represents Icertis at industry events and customer engagements.

Leads marketing strategy and execution for Heartflow Plaque Analysis, driving customer adoption and utilization. Responsibilities include developing positioning, value propositions, messaging, omnichannel campaigns, sales enablement, thought leadership, and adoption strategies. The role partners with Marketing, Sales, Medical Affairs, and field advisory teams, manages timelines, establishes performance metrics, and uses data to evaluate marketing effectiveness in the cardiovascular healthcare market.

Lead Hex’s AI engineering and research organization in defining strategy across post-training, model evaluation, search and retrieval, and continuous learning. Manage research partnerships, publish technical content, and hire and develop AI engineers and researchers. Partner with executive leadership and product and engineering teams to deliver AI-powered product experiences and business outcomes.

Technology interns complete an end-to-end project supporting a real business priority, using data, analytics, AI tools, and digital workflows. They collaborate across departments, develop practical solutions, validate AI-generated outputs, and present results to senior stakeholders. The 10-week hybrid program includes mentorship, structured training, and exposure to technology, data engineering, machine learning, IT, and analytics teams.

Plans and executes Starlink, VIP launch, customer, and employee events from concept through completion. Responsibilities include vendor coordination, sponsorship activations, agendas, registration, executive communication, onsite support, event design, budgeting, payments, shipping, inventory, gifting, and administrative process automation using AI. The role requires frequent domestic travel, extended hours, and coordination with security teams for Air Force and NASA installation access.

Own Simple AI’s enterprise revenue number, personally close strategic deals, and build a repeatable sales engine. Responsibilities include prospecting, discovery, business-case development, executive alignment, technical evaluations, negotiation, forecasting, and closing. The role also hires and coaches quota-carrying sellers, develops sales territories and playbooks, and partners with Product, Engineering, founders, and technical customer teams to sell voice AI solutions based on measurable business outcomes.

Own strategic enterprise client relationships from account strategy through delivery, renewal, and expansion. Build executive stakeholder relationships, coordinate cross-functional teams, manage risks, lead business reviews, communicate value, and identify upsell and cross-sell opportunities. Develop repeatable account-management practices, track client health and business-impact metrics, and partner with Sales and Growth leadership on commercial strategy, renewals, and expansion.

Evaluates and implements AI-enabled solutions, automated workflows, and technology improvements for legal practice areas. Partners with attorneys, practice leaders, technology teams, and vendors to assess needs, run pilots, coordinate rollouts, improve processes, and drive adoption through training and change management. The role also develops innovation strategies, evaluates emerging technologies, and prepares reports and materials for stakeholders.

Leads modeling strategy for Riot’s anti-cheat products, designing adaptive classifiers and anomaly-detection systems that identify bots, cheaters, and behavioral threats. Builds production-ready ML pipelines, prototypes novel approaches, establishes evaluation standards, improves feature and architecture strategies, and translates technical tradeoffs for cross-functional audiences. The role also mentors junior staff, supports recruiting, and bridges machine-learning research with scalable production systems.

Leads GitLab’s Agentic Software Delivery product section across continuous integration, artifact management, continuous deployment, and observability. Owns three-year strategy, product vision, business outcomes, adoption, retention, revenue, investment decisions, pricing, packaging, and go-to-market execution. Manages senior and principal product managers, partners with Engineering and commercial teams, and guides customers toward AI-powered software delivery. Requires strong technical fluency in DevOps, executive communication, portfolio leadership, and experience with AI-native product management.