Top Artificial Intelligence Jobs

Lead AI engineering teams to develop and deploy AI-powered products. Oversee AI software development, partner with cross-functional teams, and drive innovation in banking AI solutions.

Design, build, deploy, and maintain production machine learning models and pipelines at scale. Collaborate in cross-functional Agile teams to architect ML systems, automate testing and CI/CD, monitor model performance, and apply responsible and explainable AI practices. Leverage cloud platforms and distributed computing to optimize model training and serving.

The AI Data Scientist Sr designs and implements AI/ML solutions, collaborates with stakeholders, processes complex datasets, and optimizes business outcomes through data-driven analytics.

This role involves leading the development of AI-powered products, optimizing large scale AI systems, and mentoring cross-functional teams. It focuses on designing and deploying AI components, contributing to the technical vision, and applying cutting-edge AI techniques.

The Lead AI Engineer will develop and support AI software components, collaborate with cross-functional teams, and optimize large language models for performance and scalability.

Lead development and evaluation of AI products for medical professionals using large-scale datasets. Create product and client-facing analytics, influence data team strategy, and collaborate with product managers, analysts, and engineers to execute data projects from ingestion through analysis to recommendations.

The Director of Strategic Sales leads business development and partnerships across Cloud AI and Datacenter accounts, focusing on strategic sales growth and ecosystem collaboration.

Lead strategy and execution for generative and multimodal vision systems, from research to production. Design model architectures, adaptation, evaluation, and deployment pipelines; improve realism, controllability, robustness, and temporal consistency; benchmark and guide model selection; mentor scientists and coordinate cross-functional engineering to ship production-ready AI capabilities.

Configure, integrate, and deploy the BT tool stack (Snowflake, Salesforce, Streamlit, workflow tools). Build integrations and Streamlit apps, deploy AI-enabled automations, maintain Snowflake data models and reports, catalog assets with DataHub, implement SDLC/testing/guardrails, ensure data security, and partner with Engineering/InfoSec while mentoring BT team members.

Design, build, and operate AI platform components (LLM gateway, model serving, developer tooling). Own features from development to production, contribute to reliability, observability, governance, and cross-functional integration while producing technical documentation and participating in design reviews.

Lead development of production AI-assisted engineering systems: build services, APIs, integrations, and tooling; direct and review AI coding agents and AI-generated code; establish repeatable practices, observability, testing, and secure delivery for enterprise platforms.

Design and operate AI agents and decision-support systems for GTM workflows. Embed in field motions, translate stakeholder research into prioritized backlogs, ship and iterate on production solutions, monitor adoption and quality, and scale reusable intelligence across revenue teams.

Design, build, and operate AI agents and decision-support systems embedded in GTM workflows. Conduct stakeholder research, prioritize problems, ship and iterate on agent outputs, monitor adoption and quality, and scale reusable solutions across sales and customer-success motions while staying closely embedded with field teams.

Lead design, development, deployment, and support of large-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, partner with cross-functional teams, and contribute technical vision and roadmap for foundational AI systems.

Lead design, develop, deploy, and support large-scale AI systems (foundation model training, LLM inference, vector search, guardrails, observability). Partner cross-functionally to optimize performance, scalability, and cost, and shape long-term foundational AI technical vision and roadmap.

Lead design, build, deploy, and support GenAI platform services (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, collaborate cross-functionally, contribute to technical vision and roadmap.

Lead People-side AI strategy and portfolio to design, govern, and scale AI-enabled HR experiences, intelligent workflows, and agentic solutions. Partner across HR COEs, shared services, Tech, Legal, Privacy, and Risk to ensure compliant, effective deployments, drive adoption, measure value, and build a high-performing AI enablement team.

Manage and execute Responsible AI governance workflows, intake validation, backlog remediation, documentation QC, audit readiness, and reporting. Coordinate stakeholders, prioritize and escalate higher-risk AI cases, drive process improvements, and mentor junior team members while scaling governance operations.

Execute and maintain Responsible AI governance workflows: intake validation, QC of AI submissions, remediation tracking, stakeholder coordination, reporting, and operational documentation to ensure audit readiness and timely closure.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Build and productionize agentic workflows and RAG pipelines, integrate LLMs into the GTM stack, and develop high-performance backend services and ML pipelines. Connect quoting engine, Salesforce, and data lakes, solve synchronization and architectural challenges, and ensure observability, scalability, and reliability for sales automation tools.

Lead analytics engineering for Revenue, Marketing, and Finance by building scalable data solutions, data models, and dashboards; provide training and consultation, support ad hoc data needs, and translate technical findings into executive-level insights to improve operational performance.

Lead client engagements to assess business challenges, define enterprise data and AI strategies, develop roadmaps, guide solution design and delivery, provide technical leadership and mentoring, and support practice growth via presales and thought leadership.

Lead and execute AI integration across finance and legal, owning strategy, sequencing, and delivery. Build and lead embedded AI Fellows, productionalize LLM-driven tools, drive adoption, measure business impact, and coordinate with external partners to consolidate initiatives and ensure enterprise readiness.

Lead enterprise AI integration across Product Development and Product Excellence. Build unified data infrastructure, deploy AI tools, reform program management, scale AI-powered quality and consumer insights, and lead an embedded AI Fellows team to drive adoption, measurable outcomes, and organizational change across hardware and consumer product lifecycles.