Top Artificial Intelligence Jobs

Lead architecture, design, and delivery of enterprise GenAI applications. Build and deploy scalable cloud-native AI systems, mentor engineering teams, implement engineering best practices, design agentic AI solutions (LangChain/LangGraph), and manage full-stack development, deployment, security, and performance for production AI applications.

Own the vision, roadmap, and delivery of e.l.f.'s agentic commerce capabilities across AI assistants, generative search, discovery, personalization, offers, and checkout. Partner with AEO/GEO, Connected Commerce, CRM, engineering, data science, design, and analytics teams to improve product visibility, citations, and conversion. Define requirements, run experiments, measure outcomes, manage risks, and establish agent-friendly commerce experiences and integrations.

Scale and secure the enterprise Databricks platform on AWS for production analytics, ML, and AI agent workloads. Administer workspaces, Unity Catalog governance, CI/CD pipelines, Terraform automation, MCP server integration for agent frameworks, cluster policy enforcement, cost attribution, monitoring, incident response, and mentor junior engineers.

Lead and grow an AI Platform engineering team delivering cloud-native infrastructure for AI/ML development. Own people leadership, hiring, roadmap delivery, architecture reviews, engineering standards, stakeholder communication, and team wellbeing while partnering across AI, product, and cloud teams.

Build and deploy production AI and machine learning solutions using Python, foundation models, traditional ML, retrieval workflows, and synthetic data. Develop models for classification, entity resolution, ranking, prediction, and data enrichment. Own evaluation, MLOps pipelines, model monitoring, deployment automation, performance and cost optimization, and responsible data practices. Collaborate across technical and business teams while working with messy healthcare and insurance data.

Develop and implement AI/ML algorithms across computer vision, NLP, time series, graph data, reinforcement learning, and large language models. Analyze multidomain datasets, apply research findings, support AWS training and deployment pipelines, document systems, and mentor team members. The role requires Python and deep learning expertise, teamwork with version control, and the ability to obtain a TS/SCI clearance. Travel is local and up to 10%.

Develop and execute the U.S. Aesthetics AI strategy, identify AI opportunities, support high-priority pilots, monitor progress, and scale successful initiatives. Apply machine learning, generative AI, and agentic AI expertise while establishing governance, enabling technology, ensuring data readiness, and promoting responsible AI. The role requires cross-functional collaboration, operational excellence, and communication across management levels.

Leads Gilead’s US marketing AI decisioning strategy, roadmap, governance, and delivery. Develops agentic AI capabilities supporting pharmaceutical marketing strategy, insights, and activation decisions for HCP and patient tactics. Coordinates business, analytics, data, technology, and commercial stakeholders; defines AI agent context, business rules, and data requirements; establishes operating models; manages priorities and execution; and ensures measurable business outcomes, responsible AI compliance, scalability, and continuous improvement.

Designs and maintains system integrations, APIs, middleware, data pipelines, automation workflows, analytics dashboards, and AI-enabled business solutions. The role transforms complex structured and unstructured data into trusted assets, integrates LLMs and machine learning capabilities, supports reporting and visualization, evaluates emerging technologies, and partners with business stakeholders to identify process improvements. Success requires rapid learning, technical versatility, end-to-end problem solving, and effective communication across technical and executive audiences.

Design, develop, deploy, and monitor end-to-end AI and machine learning solutions for clients. Build scalable cloud architectures, optimize RAG pipelines, create retrieval evaluation frameworks, design knowledge graphs, develop data ingestion pipelines, deploy secure self-hosted models, and protect PHI/PII. Advise cross-functional teams, mentor colleagues, and contribute to technical thought leadership.

Own customer problems from concept through production by building full-stack financial systems powered by AI agents. Develop workflows for invoicing, collections, payroll, reporting, and month-end close; create integrations with banking, accounting, payroll, payment, and communication platforms; and architect secure, auditable, reliable infrastructure. Collaborate directly with founders and finance teams, design intuitive interfaces, iterate based on customer feedback, and help establish the startup’s engineering culture and technical standards.

Lead strategy, roadmaps, lifecycle management, adoption, and value realization for enterprise AI and technology products. Translate business needs into requirements, backlogs, launch plans, adoption programs, and measurable outcomes. Partner with IT, cybersecurity, architects, vendors, delivery teams, and business stakeholders to evaluate and scale AI products including Microsoft 365 Copilot, Copilot Studio, AI agents, intelligent automation, enterprise search, and knowledge solutions. Support AI governance, product metrics, user feedback, continuous improvement, and responsible product adoption.

Lead or support integration and testing of spacecraft hardware in lab and cleanroom environments. Create AI&T flows, develop and execute test procedures, use test and measurement equipment and GSE, document results, identify risks and facilities needs, and coordinate with systems engineers and external partners during spacecraft acceptance and launch operations.

Lead AI enablement and change management across the firm’s East Coast offices. Design and deliver attorney-focused training, AI academies, workshops, adoption campaigns, and communications for legal technology tools. Build stakeholder relationships, support responsible AI adoption, analyze usage data, report regional progress, address implementation barriers, and collaborate with legal technology, training, professional development, and practice group teams. The role requires autonomous regional leadership, one-to-one and group facilitation, and travel to regional and potentially international offices.

Leads AI and legal technology enablement across West Coast law firm offices. Designs training curricula, academies, workshops, communications, and adoption campaigns; manages change initiatives; builds attorney stakeholder relationships; analyzes usage data; reports progress and risks; and promotes responsible, habitual technology adoption among attorneys and practice groups.

Design and research agentic AI and reinforcement learning systems: build auto-research and coding agents, develop recursive self-improvement and multi-agent frameworks, run RL experiments (RLHF, DPO, PPO), curate training data pipelines, and publish results at top ML venues.

Build and own Snowflake’s operational tooling strategy, roadmap, and internal business systems. Design workflow automation, AI agents, and GenAI workflows for staffing, forecasting, planning, anomaly detection, and reporting. Integrate PSA, CRM, planning, and BI systems while maintaining data integrity and governance. Drive tooling adoption, standards, optimization, and cross-functional alignment with operations, enterprise technology, and data teams.

Lead and execute spacecraft assembly, integration, and test (AI&T) campaigns; define test strategies and AI&T flows; manage facilities, GSE, cost/schedule estimates; author proposals; mentor engineers and technicians; ensure quality, risk mitigation, and operational success in cleanroom and lab environments.

Build and deploy AI agents, automated workflows, integrations, analytics solutions, and internal tools across an advertising agency, initially focused on media operations. Partner with teams to identify opportunities, improve processes, ensure data reliability and governance, test solutions with users, drive adoption, and scale successful AI-enabled workflows. Evaluate emerging AI technologies and translate business needs into practical, measurable solutions.

Own strategy and delivery of AI-driven products across customer and internal use cases. Lead discovery, write requirements, partner with engineers, design experiments/A-B tests, monitor performance, and champion responsible AI practices.

Own the roadmap and end-to-end strategy for an AI-native data platform product area. Lead customer discovery, AI-native prototyping, product specifications, metrics, execution, and cross-functional alignment. Improve analyst workflows across data preparation, transformation, and analytics while ensuring reliability, explainability, governance, and enterprise readiness. Partner with engineering, design, sales, and customer success to deliver measurable customer outcomes and maintain operational product health.

The Principal AI Architect sets enterprise AI architecture direction across Aetna Technology. Responsibilities include defining model-serving, orchestration, retrieval, evaluation, and agentic-system patterns; establishing responsible AI guardrails for sensitive data; creating automated standards and paved-road development practices; measuring AI ROI and cost; evaluating emerging models and tooling; and influencing architects, engineers, leaders, vendors, and platform teams without direct authority.

The Director leads the design and delivery of AI-enabled cloud platforms at Tapestry, focusing on eCommerce. Responsibilities include platform engineering, implementing AI capabilities, promoting DevOps practices, and managing engineering teams while aligning technology with business objectives.

Own a portfolio of 6–8 strategic AI-native startup accounts, driving revenue expansion, renewals, retention, and adoption. Build relationships with founders, board members, executives, and technical leaders; lead account planning, business reviews, usage and forecast reviews, and renewal negotiations. Identify new AI use cases, manage complex multi-stakeholder sales cycles, mitigate competitive risks, gather customer feedback, and align Anthropic solutions with business objectives.

Lead cross-functional transformation programs to embed AI and automation into control management workflows. Diagnose processes, define outcomes and KPIs, build roadmaps, design people/process/technology solutions, run governance from pilot to scale, manage risks and dependencies, and measure benefits while ensuring responsible, compliant AI adoption across the firm.