Top Artificial Intelligence Jobs

Drives SEO and AI visibility for higher education clients by conducting audits, keyword and prompt research, analyzing performance data, and recommending improvements to crawlability, indexation, structured data, content, and discoverability. The role advises partners, presents insights, develops SEO and GEO strategies, monitors emerging search trends, and uses analytics and optimization tools to improve organic search and AI-driven discovery.

Build shared APIs, SDKs, evaluation infrastructure, tracing, observability, and model-routing systems for Suno’s AI agent platform. Support engineering teams adopting the platform, improve reliability and scalability with Infrastructure, and manage provider fallbacks, rate limits, and cost controls. The role requires production backend or platform experience, distributed systems expertise, and experience supporting developer teams and deploying LLM or agent systems.

Build and operate backend systems for Suno’s multi-step AI music agent. Design evaluation datasets, graders, regression sets, release gates, and data pipelines for nondeterministic systems. Create internal tools for reviewing model output, support production launches and on-call operations, and collaborate with ML engineers, product managers, and partner teams. The role also involves mentoring engineers and shipping high-traffic creative music features.

Leads the enterprise strategy and roadmap for Commercial AI, digital products, Salesforce, marketing technology, customer intelligence, data, analytics, and agentic capabilities. Partners with senior business and technology leaders to prioritize investments, shape architecture, scale AI-enabled commercial experiences, manage strategic technology partnerships, and build a high-performing product organization. Drives measurable outcomes across sales productivity, customer engagement, opportunity identification, revenue enablement, and value realization.

Leads enterprise Data and AI platform governance, operations, adoption, optimization, security, and compliance. Manages analytics strategy and team performance while overseeing data pipelines, predictive models, analytical databases, and workflows. Partners with technical and business stakeholders to translate needs into solutions, improve platform value, establish standards, and support responsible innovation. Responsibilities include recruiting, coaching, mentoring, cost optimization, vendor management, and communicating complex technical findings to diverse audiences.

Leads Zuora’s AI platform strategy, architecture, and development. Builds secure, scalable shared capabilities for AI-powered products, agentic workflows, model orchestration, retrieval, context management, and enterprise system integration. Establishes governance, evaluation, observability, reliability, cost controls, and authorization standards while translating platform investments into business outcomes. Partners across Product, Engineering, Data, Security, Legal, and Compliance, and builds a high-impact AI platform organization.

Leads development of agentic AI capabilities that execute multi-step workflows across desktop engineering tools. Owns agent architecture, evaluation frameworks, benchmarking, cost efficiency, user-story validation, and production delivery. Serves as player-coach for a small AI engineering team, writing code, reviewing designs, and setting technical direction. Requires expertise in LLM application architecture, real-world agents, Python, desktop automation, engineering software domains, and enterprise deployment constraints.

Leads the company’s AI governance program by developing frameworks, policies, controls, inventories, approval processes, and monitoring for AI and LLM tools. Partners with security, engineering, IT operations, risk, legal, and compliance teams to manage AI risks, third-party due diligence, data protection, regulatory compliance, audits, training, and responsible AI adoption. Tracks emerging regulations and standards while measuring program effectiveness and driving continuous improvement.

Own regional P&L, ROI, revenue, unit sales, gross margin, team structure, hiring, and resource allocation for Western Pennsylvania. Prioritize market verticals, lead regional sales execution, manage KAM and partner routing, establish performance targets, report pipeline and hiring progress to senior leadership, and develop investment cases for regional expansion. The role requires extensive travel and leadership of direct and channel B2B sales across multiple verticals.

Build and scale production AI innovations, including agentic systems, real-time voice, and multimodal experiences. Research models and frameworks, prototype rapidly, run safe experiments, and measure quality, latency, cost, and impact. Extend agent-platform orchestration, tool use, and evaluation capabilities. Make cross-team technical decisions, establish engineering practices, mentor others, communicate with technical and executive audiences, and partner with product, design, and engineering to turn validated concepts into secure, reliable platform products.

Build and deploy data integrations, AI-powered workflows, and internal tools. Responsibilities include connecting APIs and databases, improving data quality, implementing validation and monitoring, developing retrieval and automation applications, translating business needs into deliverables, deploying production solutions, documenting systems, and transferring knowledge to internal teams.

Own the conversational intelligence layer of a consumer application, including agent architecture, prompt design, tool calling, reasoning, memory, error handling, response rendering, model routing, and evaluation. Build dependable multi-turn language-model experiences, improve search and ranking quality, and launch personalized AI features for live users. The role requires broad applied AI expertise, strong analytical ability, and experience diagnosing production issues.

Leads Microsoft’s AI Workforce sales business across state and local government, education, academic medical centers, and software customers. Owns strategy, revenue growth, pipeline, forecasting, competitive execution, and customer adoption. Develops sales leaders and specialist teams, builds executive relationships, orchestrates Microsoft and partner resources, and drives scaled adoption of Copilot, AI agents, and cloud platforms. Establishes operating rhythms, strengthens competitive positioning, and translates AI transformation opportunities into measurable customer and business outcomes.

Design and implement production agentic AI systems, RAG pipelines, intelligent document processing services, embedding and vector retrieval workflows, and multi-agent extraction systems. Build FastAPI and Java/Spring Boot services, databases, evaluation harnesses, and governance controls. Own major AI platform components, collaborate with architecture and business teams, and mentor mid-level engineers while driving technical excellence and production quality.

Build and operate scalable AI context and knowledge platforms, including retrieval services, semantic and hybrid search, RAG, memory systems, ingestion pipelines, indexing, and context orchestration. Develop secure distributed services on Kubernetes, monitor retrieval quality and system health, evaluate emerging AI technologies, troubleshoot complex production issues, and provide technical leadership through design reviews, code reviews, mentorship, and cross-functional collaboration.

Leads enterprise AI platform strategy, engineering, operations, governance, and continuous improvement. Manages teams delivering secure, scalable AI services across cloud and on-premises environments, including model access, agent frameworks, orchestration, RAG, gateways, and observability. Establishes ModelOps, LLMOps, and AgentOps practices, reliability metrics, cost controls, and lifecycle standards. Partners with architecture, security, data, infrastructure, and governance teams while managing vendors, technology roadmaps, platform costs, and organizational stakeholders.

Identify business-focused AI/ML use cases and develop scalable solutions using machine learning, NLP, generative AI, LLMs, retrieval-augmented generation, and agentic workflows. Collaborate with data science and engineering teams on deployment, evaluation, monitoring, guardrails, and responsible AI standards. Communicate technical concepts to diverse stakeholders and deliver measurable value through practical AI/ML applications.

Design, build, evaluate, and productionize AI solutions for clinical-trial data harmonization, retrieval, and knowledge graphs. Develop auditable RAG pipelines, define systematic evaluation methods, implement human-review controls, and expose capabilities through APIs. Collaborate with architects, data engineers, and product stakeholders to integrate AI into the data-layer platform while meeting regulatory, traceability, and compliance requirements.

Lead the development and commercialization of agentic AI products. Responsibilities include customer discovery, prototyping, product requirements, UX design, pricing and positioning, go-to-market strategy, engineering collaboration, and launch. The role focuses on translating Mysten Labs’ AI and blockchain capabilities into customer-ready products, defining permissions and oversight experiences, validating demand, and driving adoption, conversion, and revenue.

Leads a multidisciplinary team building secure, production-ready AI applications and agent solutions for federal mission support. Provides technical direction across architecture, development, testing, deployment, operations, RAG, integrations, evaluation, observability, and responsible AI. Translates mission needs into roadmaps and delivery plans, establishes Python and secure software engineering standards, coordinates cybersecurity and cloud requirements, manages priorities and risks, and develops AI safety and alignment practices.

Designs and supports enterprise automation and AI solutions using Power Automate, RPA, Python, and AI tools. Leads integrations, develops workflow automations, and supports digital transformation initiatives. Manages transitions from implementation to production, provides Tier 2/3 application support, troubleshoots enterprise systems, and documents and trains users. Ensures compliance with data governance, security, and integration standards while collaborating with academic, administrative, IT, and business teams.

Build and own the recruiting marketplace’s AI/ML intelligence layer, including recommendation, ranking, retrieval, and LLM-powered candidate-role matching. Ship production features rapidly, architect data pipelines and model services, and translate founder product goals into deployed systems. The role requires applied machine learning, recommender systems, production LLM API experience, and strong Python or TypeScript skills. Work is fully on-site in San Francisco six days per week.

Design and operationalize security architecture, governance patterns, and technology guardrails for enterprise AI platforms, agentic systems, cloud, SaaS, and emerging technologies. Create reference architectures, diagrams, threat models, security requirements, standards, and implementation guidance. Partner with engineering, identity, application security, infrastructure, data, risk, and architecture teams to assess designs, facilitate technical reviews, evaluate emerging technologies, and enable secure AI adoption at scale.

Build and deploy production AI agents using tool-calling LLMs, structured outputs, API orchestration, and streaming responses. Develop evaluation harnesses, monitor quality and traces, and improve reliability, latency, and cost through caching, token budgets, retries, and observability. Create validated agent tools and integrations while collaborating with product, mobile, and backend teams. Own the full lifecycle from prototyping and evaluation through staging, production deployment, monitoring, and continuous improvement.

Leads the practical application of generative and agentic AI across the firm. Partners with business and technology stakeholders to identify opportunities, design secure and scalable AI architectures, evaluate technical tradeoffs, develop reusable patterns, and guide solutions from discovery through production. Provides architectural standards, prototypes, evaluation methods, and engineering handoff support while ensuring alignment with security, data, operational, cost, and business requirements.