Top Artificial Intelligence Jobs

Lead strategy, roadmap, and delivery for AI, data, and analytics products supporting commercial operations. Drive AI use cases, scale generative AI and automation, manage product backlogs, lead Agile teams, ensure responsible AI governance, and align investments to business value with cross-functional stakeholders.

Senior HR business partner leading HR for global Business AI, Technology & Product. Drive organizational design, workforce planning, leadership development, talent strategy, employee relations, performance and rewards, compliance, HR operations, and coach HR teams to enable growth and transformation across a complex, technology-focused global organization.

The Director of AI Solutions & Integration leads the development of production-ready AI solutions at SPDJI, ensuring alignment with business goals and responsible AI practices.

Lead end-to-end, high-impact implementations of a conversational AI platform for financial institutions. Drive strategy, stakeholder alignment, risk mitigation, solution design, testing, documentation, compliance fit, and mentor other implementation managers while improving processes and tooling.

Lead early-stage AI/ML research concept development and sponsor engagement with U.S. government R&D agencies. Shape and capture competitive proposals, guide technical teams through experimentation and proposal delivery, then transition into program leadership to oversee execution, milestones, and analytic evaluation. Share results via publications, patents, and presentations.

Manage strategic AI and data partnerships by bridging revenue, product, and technical teams. Design solutions with Sales, lead due diligence and proposals, drive partner onboarding and growth, act as subject-matter expert on AI/data licensing, run business reviews, and capture partner feedback to influence roadmap and opportunities.

Build, deploy, and optimize production-grade generative AI solutions using LLMs, prompt workflows, RAG pipelines, vector search, and agent-based systems. Integrate models with enterprise data sources and cloud platforms, apply LLMOps practices (testing, monitoring, CI/CD), collaborate with architects and data scientists, and mentor junior engineers while ensuring performance, reliability, and cost efficiency.

Plan, conduct, and synthesize end-to-end qualitative and quantitative UX research for AI-powered productivity features. Run usability studies, interviews, surveys, and benchmarking; translate findings into actionable recommendations; present to stakeholders; collaborate with product, design, and engineering; evaluate usability and accessibility; leverage AI tools to improve research workflows; manage multiple projects on tight timelines.

Design, train, and deploy reinforcement learning systems for decision-making problems. Build and scale simulation environments, design reward functions, train neural-policy models on GPU clusters, evaluate and productionize RL solutions with emphasis on safety, stability, and ongoing improvement.

Build and operate petabyte-scale data pipelines for AI training and evaluation: ingestion, cleaning, versioning, lineage, high-throughput loading, labeling workflows, privacy controls, observability, and storage architecture optimization while collaborating with ML researchers.

Lead impartial workplace investigations and ensure EEO compliance while applying AI-enabled analytics to identify trends and risks. Maintain case documentation, prepare investigative reports, develop dashboards and KPIs, evaluate AI insights for fairness and legal defensibility, and provide data-driven recommendations to improve compliance, reporting, and workplace culture.

Design, deploy, operate, and optimize enterprise AI solutions on Azure. Build MLOps pipelines, monitor model performance and drift, manage versioned rollouts and rollbacks, tune inference performance and cost, and ensure secure, auditable, governed AI deployments.

Architect, build, and operationalize production-grade generative AI and ML systems across Snowflake, Databricks, and Azure ML. Implement RAG, vector search, agentic AI, and API-based model services; create CI/CD, MLOps/LLMOps practices, observability, and governance. Mentor engineers, troubleshoot inference and drift issues, and integrate AI services into regulated healthcare products ensuring security and compliance.

Lead strategic storytelling for Enterprise AI by translating complex AI strategy and capabilities into clear, executive-facing narratives and assets. Create presentations, digital content, and change communications to drive adoption and scale across business units. Partner with product, engineering, and change teams to craft measurable, business-focused stories that enable executive alignment and enterprise-wide AI enablement.

Design, prototype, and operationalize AI/ML solutions (including LLMs and RAG) for a USG program. Curate data pipelines from enterprise financial and scheduling datasets, lead LLM training/fine-tuning, scout commercial AI technologies, draft SOWs, evaluate contractor deliverables, and advise on secure deployment and model lifecycle management in high-security environments.

Lead design and implementation of agentic AI systems to automate data-science pipelines—data elicitation, curation, experimentation, model training/evaluation, diagnostics, and benchmarks. Collaborate with software and solutions teams and integrate solutions into enterprise projects and products.

Lead product programs for clinical cloud software and AI solutions for spine and cranial treatments. Define product vision, roadmaps, and business cases; translate surgeon and market insights into requirements; align technical capabilities with clinical workflows; drive go-to-market and cross-functional commercialization; and influence stakeholders while managing matrixed teams and vendors to deliver AI-enabled surgical solutions.

Build and operate core AI agent infrastructure: orchestration, context management, filesystem and sandboxed execution, model routing and serving, databases, Kubernetes, networking, and cloud systems to make long-running agent workloads fast, reliable, safe, and cost-efficient.

Senior technical leader responsible for enterprise AI and data architecture. Own the AI/data strategy and architecture roadmap, build scalable cloud-native data and AI platforms (lakehouse, data mesh), establish MLOps/LLMOps and model lifecycle practices, enforce data governance and responsible AI, partner with security and product teams, and mentor architecture and engineering teams to drive production-grade AI adoption.

Lead end-to-end development and scaling of Jerry.ai's customer-facing Agentic AI platform. Own roadmap, partner with engineering on prompt strategies, evaluation frameworks, and guardrails, drive experiments to improve automation and satisfaction, translate technical concepts across the organization, and collaborate with partners (e.g., OpenAI) to deploy voice models and workflow automation.

Lead consolidation and platform strategy for Jerry.ai's AI agent products. Own prompt consolidation, technical prioritization, API and system architecture decisions, OpenAI partnership and ChatGPT store presence, and coordinate engineering, data, and operations while maintaining the current AI platform and driving roadmap execution.

Lead consolidation and strategy for a fragmented AI prompt/platform ecosystem, prioritize chatbot work, own OpenAI/ChatGPT integrations, coordinate cross-functional teams, maintain current AI platform operations, and drive decisions on emerging tech like voicebots.

Lead development and scaling of a customer-facing AI agents platform. Own roadmap, prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and OpenAI to deploy voice and automation models, improve automation rates and answer quality, and drive experiments using data and SQL.

Lead product development and scaling of Jerry.ai's AI agent platform. Define roadmap, design prompt strategies and guardrails, partner with engineering and OpenAI, run experiments to improve automation, answer quality, and customer satisfaction, and establish platform standards and evaluation frameworks.

Lead development and scaling of an agentic AI platform that automates customer sales and service using LLMs, human-in-the-loop feedback, and APIs. Drive roadmap, prompt strategies, evaluation frameworks, guardrails, and partnerships (e.g., OpenAI) to improve automation rates, quality, and customer satisfaction.