Top Artificial Intelligence Jobs

Own a portfolio of complex MSP partners across North America, developing strategic account plans, driving product adoption, leading expansion and cross-sell opportunities, supporting renewals and commercial conversations, recovering at-risk accounts, and mentoring junior Partner Success Managers. The role requires strong stakeholder management, data-driven partner health analysis, and familiarity with B2B or SaaS accounts, cybersecurity, Microsoft 365, or the MSP market.

Embed with product teams to use data science, machine learning, experimentation, and analytics to improve user growth. Build scalable data products, models, dashboards, and analytical foundations; prioritize opportunities and assess ROI; translate complex findings into actionable recommendations; collaborate with Product, Engineering, Design, Marketing, and Finance; and mentor other Data Scientists.

Owns full-cycle recruiting across GTM and technical roles, partnering with leadership on role definition, hiring strategy, interview design, talent calibration, sourcing, candidate experience, and closing. The Lead Recruiter will build proactive pipelines, use funnel data and market intelligence to guide decisions, improve recruiting systems, influence senior stakeholders, and help shape employer brand and compensation practices in a high-growth AI company.

Develop production software using AI-assisted and Spec-Driven Development practices. Implement features from approved specifications, evaluate AI-generated code, handle integrations and edge cases, maintain automated testing, and contribute to architecture. Ensure systems are secure, auditable, performant, observable, and compliant with GxP expectations. Work across backend and frontend technologies within small, collaborative squads.

Build production-grade AI software, microservices, libraries, and data pipelines. Partner with data and backend teams to operationalize AI models, expose capabilities through APIs, deploy workloads using cloud infrastructure and Kubernetes, and maintain testing, code quality, and CI/CD standards. The role also involves ETL/ELT, database integration, and understanding AI concepts including inference, embeddings, LLMs, and RAG architectures.

Own the architecture and hands-on development of Fanatics’ enterprise AI/ML platform, including the LLM gateway, agent and MCP runtime, retrieval infrastructure, evaluation systems, governance, and cost analytics. Set technical standards, make build-versus-buy decisions, deliver scalable platform capabilities, improve observability and security, mentor engineers, and communicate architectural trade-offs to senior leadership.

Leads NVIDIA’s embedded GPU and long-life board product portfolio, managing customer relationships, design wins, pricing, supply commitments, lifecycle planning, and product roadmaps. The role coordinates Engineering, Sales, Finance, Operations, campaign teams, and partners through product introduction, launch, long-life support, and end-of-life transitions. It requires advanced hardware and software platform experience, commercial acumen, lifecycle management expertise, strong communication, and at least 25% travel.

Manage the operational delivery of human data for a strategic AI customer. Responsibilities include translating requirements into workflows, managing participant pools and onboarding, tracking supply and quality, leading cross-functional projects, resolving operational issues, documenting processes, and improving scalability with Product and Engineering. The role also supports participant assessment and certification programs while serving as an operational point of contact for delivery teams.

Owns high-impact U.S. regional projects spanning sales, partnerships, delivery, and customer outcomes. Translates leadership priorities into workstreams, milestones, decisions, and measurable results; advances enterprise opportunities, manages customer and delivery risks, develops partner and segment strategies, and builds dashboards, operating cadences, playbooks, and AI-enabled automation. The role requires cross-functional leadership without formal authority and close engagement with customers, partners, and frontline teams.

Leads enterprise AI-first technology delivery transformation at MetLife. Defines operating models, roadmaps, governance, controls, adoption strategies, delivery metrics, and readiness practices for agentic software development. Partners with CIO organizations, DevSecOps, Platform Engineering, Architecture, Risk, Audit, and Legal to implement traceable, reviewable AI-assisted workflows in a regulated environment. Advises senior leaders, resolves cross-organizational ambiguity, and drives initiatives through measurable adoption and business value realization.

Designs and maintains MetLife Global Technology’s AI-first operating model, including team archetypes, role and accountability frameworks, human-agent governance, lifecycle architecture, decision rights, and transition plans. Collaborates with technology, risk, audit, architecture, and delivery leaders to create standards and reference artifacts that support adoption across CIO organizations. The role also facilitates implementation, incorporates field feedback, and evaluates operating-model effectiveness in regulated technology environments.

Build, secure, and maintain Azure and GCP cloud infrastructure across environments. Manage security controls, compliance, CI/CD, automation, monitoring, and deployments while integrating cloud services with software applications. Collaborate with engineering teams using AI-assisted development tools and LLMs. Support production software, AI agents, retrieval systems, and RAG applications. The role requires strong problem-solving, communication, collaboration, and independent delivery skills.

Build unified structured and unstructured data pipelines, predictive and prescriptive analytics, churn models with quantified uncertainty, and production LLM agents. Own enterprise AI architectures, RAG systems, MLOps and LLMOps standards, evaluation, monitoring, cost optimization, security, and reliability. Partner with business stakeholders to translate ambiguous problems into scalable AI solutions and document reference architectures adopted by other teams.

Administer enterprise IT systems and SaaS platforms while deploying and governing AI tools, MCP servers, and agent infrastructure. Manage service accounts, credentials, permissions, integrations, onboarding and offboarding, automation, documentation, and technical debt. Serve as an escalation point for complex ITSM issues, collaborate across Security, People Ops, and Engineering, and mentor IT Support staff on processes and troubleshooting.

Own new-logo growth and account expansion across North American upstream E&P and midstream operators. Build pipeline through an established Energy network, develop senior client relationships in Houston, position analytics and AI offerings, and lead proposals and pursuits through close. Translate data, IT/OT, IoT, predictive asset performance, and production optimization capabilities into client outcomes while representing the firm at industry events and informing practice strategy.

Lead a team of customer success and implementation professionals responsible for deploying an AI platform across large capital programs. Define customer use cases and data strategies, accelerate implementation and adoption, manage executive relationships, establish delivery playbooks, and measure business value. Own renewal forecasting, negotiations, retention, and expansion while partnering with Product and Engineering on customer-driven roadmap priorities. Coach early-career employees and improve deployment consistency, time to value, and account outcomes.

Leads enterprise AI and ML engineering strategy, architecture, and implementation. Oversees cloud-native platforms, MLOps, model deployment, inference, monitoring, data lakes, RAG applications, and intelligent automation. Provides technical leadership across multiple teams, establishes engineering standards, advises senior management, drives organization-wide initiatives, and mentors staff. The role requires extensive AWS, Python, machine learning, data engineering, CI/CD, and generative AI expertise in a financial services environment.

Architects and develops secure, scalable AI/ML systems and MLOps infrastructure across distributed cloud environments. Builds model training, inference, monitoring, governance, adversarial robustness, federated learning, and multi-agent safety capabilities. Designs agentic workflows, data and ETL pipelines, model lifecycle automation, and high-performance serving systems. Evaluates reliability and security under real-world and adversarial conditions, collaborates with cybersecurity and cloud teams, and mentors engineering teams on advanced machine learning and secure development practices.

Design, build, and deploy agentic AI solutions (Google Gemini, Claude, ADK); maintain BigQuery data warehouse and ETL pipelines; troubleshoot tracking across Google Analytics, Omeda, Piano, and Salesforce; create dashboards and monitoring to ensure data quality and measure agentic impact across Sales, Marketing, Editorial, and Finance.

Leads research and client advisory on securing artificial intelligence and generative AI systems. Develops written research, presentations, market analysis, and actionable cybersecurity guidance; evaluates AI security controls, standards, governance practices, and supporting technology markets. Represents Gartner as an industry expert, supports sales and research teams, reviews content, presents at events, and collaborates with research peers.

Lead end-to-end delivery of a complex regulator-facing data and AI modernization program. Responsibilities include program governance, stakeholder engagement, budgeting, forecasting, contract performance, Agile execution, roadmap and release planning, resource management, risk mitigation, quality oversight, and executive reporting across technical and business workstreams.

Designs and governs enterprise data architectures supporting analytics, reporting, and AI initiatives. Leads data modeling, ingestion, transformation, storage, and consumption architecture across AWS and Snowflake. Establishes architecture standards, supports data governance, metadata, quality, lineage, and stewardship, and translates business and regulatory requirements into scalable solutions. Partners with cloud architects, engineers, delivery teams, and stakeholders to ensure implementation quality and alignment.

Design, develop, and deploy agentic AI and ML systems for military intelligence. Build LLM/NLP pipelines, retrieval-augmented agents, evaluation frameworks, and secure production prototypes. Collaborate with analysts and stakeholders to translate mission requirements into explainable, robust AI capabilities for classified and operational environments.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Lead client advisory, strategy, design, delivery, and optimization of AI and automation solutions for healthcare payer workflows. Facilitate discovery workshops, develop adoption roadmaps, design responsible AI architectures, establish governance and human-in-the-loop controls, evaluate platforms and vendors, support pre-sales, and coordinate cross-functional delivery. Apply technologies including large language models, retrieval-augmented generation, workflow automation, copilots, and agentic AI in regulated healthcare environments.