Top Artificial Intelligence Jobs

Lead research on agentic AI, developing self-improving, multi-agent systems and continual learning methods. Design evaluation frameworks, synthetic data pipelines, and scalable architectures to move research into deployed products while collaborating with engineers and product teams.

Operate and improve large-scale HPC GPU clusters for AI model training and inference. Build observability, automation, CI/CD, and incident response tooling; lead on-call rotations, ensure security/compliance, collaborate with ML engineers, and optimize capacity and costs for production ML workloads.

Lead development of scalable stress, validation, and performance tooling for MAIA AI accelerator platforms. Build workload generation, kernel optimization, compiler/SDK integration, automated orchestration and telemetry, platform validation suites, benchmarking, and CI-driven regression infrastructure to enable platform bring-up, reliability, and performance optimization across hardware and software stacks.

Lead enterprise-scale AI transformation programs: design lighthouse pilots, translate strategy into roadmaps, establish governance and change management, mentor teams, drive cross-functional delivery, and codify playbooks for sustained adoption.

Design, integrate, validate, and optimize large-scale AI networking infrastructure. Lead Layer 3/4 and RDMA-based fabric architecture, performance characterization, congestion management, packet-level debugging, telemetry, automation, and deployment for hyperscale AI training and inference clusters.

Lead vision, strategy, and roadmap for AI-driven creative advertising products. Partner with Data Science, Engineering, UX, Marketing, Legal, and Finance to build generative-asset pipelines, creative automation, quality systems, and go-to-market plans that drive advertiser performance, scale creative production, and protect brand integrity.

Design and deliver secure, scalable Azure and AI-ready infrastructure. Lead architecture sessions, drive migrations and modernization (infrastructure, databases, SAP, hybrid), ensure operational excellence, create repeatable IP, resolve technical blockers, and accelerate customer adoption and business value.

Lead cross-organization technical programs for Azure Specialized Compute, supporting customer cloud migrations and high-performance/GPU workloads. Drive end-to-end initiatives, mentor junior team members, and coordinate with product and engineering teams to deliver scalable compute services.

Own the roadmap and delivery of AI automation products, including reliable AI agents, tool-calling workflows, n8n automation pipelines, API integrations, and React-based interfaces. Partner with sales, content, and marketing to identify opportunities, then monitor, debug, and improve shipped solutions. The role requires strong product ownership, hands-on AI and automation experience, technical range across frontend and integrations, and fluency with LLMs and agent frameworks.

Design and build AI-enabled applications centered on knowledge graphs, ontologies, graph and vector databases, RAG, and agentic pipelines. Develop scalable backend systems, APIs, data pipelines, integrations, and interactive full-stack web applications. Make architectural decisions, integrate intelligent search and retrieval features, evaluate emerging technologies, and ship production code. Collaborate with product, design, domain experts, and leadership while contributing to engineering standards, quality, security, testing, and maintainability.

Leads Anthropic-focused client accounts from activation through delivery and expansion. Builds account plans, AI use-case backlogs, roadmaps, and multi-phase transformation programs; oversees delivery quality and risk; advises senior technology and business stakeholders; partners with sales and Anthropic teams to grow accounts; leads executive meetings and mentors team members. Requires strong consulting, AI/data, account growth, and engagement-scoping experience, with travel up to 50%.

Lead Americas Hyperscale & AI sales strategy and planning; build and mentor a strategy team; develop annual and long-range GTM plans; drive transformation initiatives; provide data-driven competitive insights using AI/BI and statistical tools; advise executive leadership and align cross-functional teams to optimize resource allocation and sales organization design.

Lead Cisco’s AI marketing strategy and pipeline, drive developer and enterprise buyer engagement, design executive AI summits, scale global event activations, influence industry narratives with analyst relations, and manage cross-functional marketing programs to convert market moments into measurable pipeline and revenue.

Build and maintain scalable batch and real-time data pipelines and services for finance systems. Develop ETL workflows, data models, APIs, and distributed processing solutions using Spark, cloud platforms, and SQL. Ensure data governance, security, and quality while collaborating with engineers, product teams, and business stakeholders to troubleshoot, optimize, and improve system reliability.

Lead the design and delivery of AI solutions for legal professionals. Mentor engineers, establish AI engineering practices, and ensure scalable architecture and quality across systems.

As a Software Engineer - AI, you will design, develop, and deploy AI solutions for legal applications, working with clients and technical teams to enhance legal workflows and improve AI outputs.

Own the Investment Strategy Group’s agentic AI platform from vendor strategy and build-versus-buy decisions through production deployment. Build research, due diligence, portfolio monitoring, and advisor content agents; maintain the shared research knowledge base; establish human-review guardrails, compliance controls, audit trails, and risk management. Partner with investment, technology, compliance, and wealth teams to drive advisor adoption, measure performance and efficiency, and advise leadership on AI capabilities and roadmap priorities.

Build AI-enabled automation, Excel/VBA tools, Power Automate workflows, and Azure-based solutions supporting buy-side and sell-side due diligence. Analyze and transform complex financial datasets, develop agentic AI workflows, create dashboards and reporting outputs, and perform testing and reconciliation. Collaborate with Transaction Services and Data Intelligence Services teams to improve recurring processes, document solutions, and deliver accurate insights under tight deadlines.

Design, develop, test, operate, and maintain AI-enabled software and infrastructure. Build production-ready code, RAG pipelines, vector search integrations, and agentic AI components. Follow SRE/DevOps and responsible-AI practices, participate in code reviews, troubleshoot model quality, and collaborate across engineering, product, and business teams to deliver scalable GenAI solutions.

Founding Full-Stack AI Engineer to architect, build, and scale an enterprise digital account manager. Own end-to-end features, build integrations and data ontology mapping, develop intuitive UIs, and design agentic LLM solutions (memory, RL). Work directly with founders and customers to iterate rapidly and deliver enterprise-grade, scalable AI products onsite in San Francisco.

Lead and grow an enterprise data, BI, and AI team to deliver scalable data pipelines, models, dashboards, and governance. Translate business needs into prioritized data products, partner with stakeholders and engineering, enforce data quality and access controls, and align initiatives to enterprise strategy and financial plans.

Design, build, and deploy agentic AI and LLM-based systems for life sciences applications. Lead research-to-production work on multi-agent systems, prompt optimization, evaluation pipelines, NLP/retrieval, observability, and cross-functional product delivery. Mentor engineers and represent the company in publications, conferences, and open-source contributions.

Lead the architecture and delivery of enterprise Generative AI solutions using LLMs, RAG, AI agents, cloud-native platforms, and enterprise integrations. Define AI strategies, reference architectures, governance, security, observability, and implementation roadmaps. Guide architecture reviews, mentor engineering teams, evaluate emerging technologies, and drive production delivery across regulated and enterprise environments.

Lead and scale a centralized AI organization for Cell Therapy Discovery and Targeted Immune Engagers: define AI strategy, prioritize and deliver initiatives (agentic knowledge hubs, predictive CAR-T models, in silico protein/binder design, CRISPR off-target workflows), embed AI across R&D, automate workflows, build multi-site teams, ensure governance/compliance, drive KPIs and executive communication, and upskill scientific teams.

Designs, develops, and deploys production Generative AI and LLM applications, including RAG pipelines, AI agents, semantic search, backend APIs, and enterprise data integrations. Builds scalable data and MLOps solutions using Databricks, Snowflake, and AWS. Collaborates across engineering and data teams while optimizing model performance, cost, latency, security, compliance, and privacy, including HIPAA requirements where applicable.