Top Artificial Intelligence Jobs

Build and scale backend services, platform infrastructure (serverless), and APIs powering financial workflows for clinical development. Integrate with LLM agents and AI workflows, implement frontend components as needed, resolve production incidents, contribute to architecture, and mentor teammates.

Manage the Core AI Platform team at Front, overseeing AI infrastructure and guiding strategic investments in AI, while mentoring senior engineers and collaborating across teams.

The AI Artist will enhance AI-generated videos, develop scalable prompts, maintain high output quality, and explore emerging AI tools.

Administer, secure, and support enterprise AI platforms (Microsoft Copilot, Claude, Azure-hosted solutions). Manage integrations, access controls, RBAC, audit logging, cost monitoring, and Tier 2/3 incident resolution. Coordinate vendors, maintain documentation, configure Azure environments, and ensure compliance with firm governance and data protection policies. Provide occasional after-hours and training-related travel support.

The AVP, Business Analytics & AI leads the execution of data, analytics, and AI strategies, driving measurable business outcomes and ensuring governance of data products and AI operations while partnering with business and technology leaders.

Lead PJT's AI security and risk program: own AI risk framework and policies, evaluate vendors and internal AI deployments, perform AI threat modeling, embed AI security controls, monitor regulation and report to leadership, support audits, and deliver awareness training. Collaborate with Legal, Compliance, Procurement, and Technology to manage model, data, and third-party AI risks across the firm.

Build, operate, and secure the firm's AI infrastructure and developer tooling to enable Python-based AI applications. Implement Azure AI Foundry, integrate LLM APIs (Anthropic, Azure OpenAI), design data pipelines (ADF, Databricks), manage cloud infrastructure (IaC, Kubernetes), and enforce security, governance, and compliance for regulated financial environments.

Work with senior leadership to drive firm-wide AI adoption: study team workflows, identify high-leverage AI use cases, design and deliver training, recruit internal evangelists, evaluate tools/vendors, track adoption, and scale best practices across global offices.

The AI and Cloud Solutions Supervisor will lead cloud and AI engineering teams, manage cloud infrastructure, and implement AI-powered tools within a hands-on technical role.

The Business Development Representative at Zentric AI focuses on generating leads, nurturing client relationships, and supporting revenue growth in service-based industries such as healthcare and real estate.

As an AI Engineer at Extend, you'll build advanced document processing infrastructures and AI product features, impacting customer operations significantly.

Lead the product strategy for Scribd's internal developer and agentic platforms: unify AI workstreams, own internal app/plugin/agent runtimes, translate functional needs into platform requirements, drive cross-functional adoption and metrics, and run internal rollouts and evaluation to prove impact.

Build and integrate AI agents and automation workflows, extend full-stack apps with LLMs, manage data pipelines (Snowflake, Postgres), support server-side event tracking, analytics and dashboards, ensure secure HIPAA/SOC2-compliant handling of PII, and collaborate with leadership to deploy AI-driven marketing and compliance solutions.

Lead a multidisciplinary AI research engineering team to build, train, fine-tune, deploy, and monitor large-scale ML models (including LLMs and recommender systems), drive experiments, collaborate with cross-functional partners, mentor engineers, and influence product and technical strategy for the Language Learning group.

Lead a multidisciplinary AI research engineering team to develop, train, deploy, and monitor ML models (including LLMs, personalization, and recommenders). Drive cross-functional alignment, mentor engineers, set technical direction, run experiments, and ensure scalable, high-quality ML solutions.

Develop and manage the research infrastructure for AI at Databricks, focusing on large-scale training, orchestration, and improving developer productivity.

The Revenue AI Solutions Analyst designs, builds, and operationalizes AI solutions that enhance Sales, Operations, and Finance efficiency through predictive models and integration with enterprise systems.

Lead and scale digital growth: own SEO, website/Shopify content, AI-assisted content, email and paid campaigns, CRM automation, lead gen, reporting, and marketing performance. Build funnels, manage vendors, present insights, and implement AI-driven tools to drive inbound leads and pipeline.

Act as a founding growth operator using Hilbert to solve live B2C growth problems, identify model failure modes, translate insights into product improvements, partner with sales to convert outputs into growth strategies, and represent the product in high-stakes GTM conversations.

Design, build, and maintain production-grade AI systems and agent-based workflows; own end-to-end pipelines from experimentation to deployment and monitoring; build evaluation pipelines; collaborate with product, data, and GTM; iterate quickly under ambiguity to shape the AI stack.

The role involves evaluating and annotating AI-generated content while ensuring cultural relevance and accuracy. It requires attention to detail and strong analytical skills.

The role involves evaluating AI outputs, generating content, providing annotations, and ensuring quality through cultural and contextual awareness.

Seeking detail-oriented reviewers for AI training projects, evaluating outputs, generating content, and ensuring cultural relevance. Requires strong analytical skills and attention to detail.

Review, annotate, and generate Spanish-language content for AI training across text, audio, and video. Evaluate AI outputs for accuracy, cultural appropriateness, and brand alignment; provide structured annotations, participate in QA and calibration, and spot translation or visual issues to improve models.

Evaluate and review AI outputs, generate or assess content, and ensure cultural relevance and accuracy in various media for AI projects.