Top Artificial Intelligence Jobs

Lead day-to-day delivery of enterprise AI projects, manage plans, milestones, risks, and stakeholder communication. Maintain Jira and documentation, run project cadences, resolve blockers, report progress to customers, build technical fluency in agents/RAG/knowledge bases/classification/extraction, and partner on project financials and closeout.

Own and optimize the customer trial experience to increase conversion and retention. Analyze engagement/usage data, identify friction points, lead quality initiatives, and improve call center and support workflows through process redesign, automation, and performance tracking. Collaborate cross-functionally to implement scalable systems.

Own end-to-end product marketing for Handshake AI's frontier lab business: create positioning, translate research to market content, enable sales with playbooks and assets, shape GTM strategy, and drive event and field marketing.

Design, develop, deploy, and monitor enterprise AI solutions (LLMs, RAG, agents) integrated with applications and databases. Collaborate with stakeholders, implement prompt and retrieval strategies, ensure Responsible AI compliance, build AI-driven monitoring and predictive analytics, and optimize models and workflows for operational efficiency.

Lead design and implementation of enterprise AI solutions and Foundry architecture. Architect cloud-based, secure, scalable data pipelines, integrations, microservices, and databases. Guide engineering teams, perform technical discovery, run design reviews and proofs-of-concept, and ensure solutions meet business, security, and enterprise architecture requirements.

Lead deployment and optimization of AI-powered contract review tools; evaluate contracts for risks using AI; collaborate with attorneys to revise language; develop templates and clause libraries; train legal staff; ensure secure AI integration and compliance with data-privacy and AI-ethics; track metrics and assist with complex negotiations and contract management workflows.

Design, build, and deploy a multi-tenant AI agency platform for pharmaceutical brand operations. Develop backend services, APIs, and front-end apps; integrate with Veeva, SFMC, SharePoint, Snowflake, and Vertex AI; implement secure authentication, RBAC, audit logging, and compliance controls; automate CI/CD, containerization, and orchestration; collaborate across Product, Data, DevOps, and Compliance to deliver scalable, traceable solutions.

Design, build, and maintain TypeScript-based AWS Lambda REST APIs and cloud infrastructure for conversational AI voice agents. Develop and optimize AI agents (ElevenLabs, ZVA), perform prompt engineering, integrate telephony (Twilio), implement IaC with AWS CDK, and apply TDD and observability to support real-time contact center operations.

Lead design, development, and testing of backend services and APIs (primarily Rust) for quantum security products. Own architecture, reliability, performance, and quality; use AI-assisted tooling; mentor engineers; collaborate across hardware, product, and engineering.

Lead and deliver high-value AI/ML consulting engagements: design, build, and deploy ML models and ETL pipelines; analyze large disparate datasets; create visualizations and executive presentations; develop business and client relationships while managing project teams and driving measurable client outcomes.

Lead AI/ML consulting engagements: design and implement ETL, data modeling, predictive models, anomaly detection, distributed data pipelines, and executive-ready visualizations. Drive business development, mentor teams, and deliver client-facing solutions in hybrid/onsite environments.

Lead AI/ML consulting engagements, build and deploy machine learning models and ETL pipelines, analyze large datasets, design data architectures, deliver visualizations, drive business development, and manage client-facing teams in high-stakes situations.

Lead discovery and build AI-enabled operational workflows across R&D and business functions. Translate requirements into practical solutions, own design-to-launch, optimize adoption, and manage Marketing Operations (campaigns, automation, tech stack). Measure outcomes, document solutions, and ensure alignment with shared AI standards.

Develop and integrate AI agents and ML models to optimize VLSI/chip design flows. Design algorithms, build software tools and frameworks, analyze data to inform agent-driven methodologies, and collaborate with VLSI, ML, and software teams to improve productivity and quality.

Design and validate scalable on‑prem datacenter and cluster reference architectures for enterprise AI systems. Lead multidisciplinary projects across networking, compute, storage, and cloud-native software to produce secure, resilient, and cost‑optimized hybrid AI solutions.

Own global channel strategy and execution for NVIDIA Jetson and IGX platforms. Manage distributor relationships, drive partner onboarding, pipeline creation, revenue, and partner readiness. Collaborate with marketing on channel programs, MDF-funded activities, demand generation, and performance measurement through reviews, scorecards, and dashboards.

Design, implement, and operate a private, secure AI platform and autonomous agent workforce for engineering workflows. Build agents (FastAPI/CLI/chat), integrate MCP and RAG, maintain containerized infrastructure, CI/CD validation, secrets and cost management, and enterprise integrations (GitHub, Jira, Confluence, Teams). Continuously evaluate models, improve agent reliability, and drive platform adoption across low-level systems engineering domains.

Lead research on agentic systems, agent ecosystems, advanced agentic capabilities, human-agent interaction, evaluation tooling, and responsible AI. Build and deploy prototypes, collaborate across teams, publish research, and translate innovations into practical, impactful applications.

Lead technical pre-sales engagements to design secure, scalable Azure architectures for migration, modernization, and AI workloads. Build trusted advisor relationships, drive POCs, apply CAF/CMF best practices, design hybrid networking and containerization solutions (AKS, Arc, AVS), and collaborate with engineering and partner teams to overcome technical objections and optimize enterprise migrations.

Design, build, and scale production-grade AI applications and agent-based systems. Lead technical customer engagements, create reusable solution accelerators, drive adoption, ensure production readiness (security, scalability, observability), and influence product roadmap while scaling field capabilities and delivery patterns.

Lead consultative sales engagements to drive adoption and scaling of AI Workforce solutions (M365 Copilot, Copilot Studio, Copilot Chat, Agents, ME3/ME5, Frontline Worker). Orchestrate virtual teams, develop stakeholder strategies, identify and pursue opportunities, present technical value propositions, and collaborate with internal teams and partners to cross-sell and deliver end-to-end AI workforce transformations.

Own product marketing strategy and execution for AI laptops/desktops/workstations. Develop positioning, messaging, launch and go-to-market plans; translate technical AI/NPU capabilities into customer value; create sales and communications assets; coordinate regional localization; track product performance; support PR, events, and cross-functional stakeholders throughout the product lifecycle.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Lead and execute strategy for AI, data, and digital assets in lending, driving innovation and delivering scalable solutions while managing cross-functional teams.

Lead applied collaborations bringing quantum-native generative methods into partner domains: design benchmarks, prototype and validate proofs-of-concept, benchmark against trusted reference methods, produce reproducible evidence and resource models, and communicate technical results to partners while informing internal research directions.