Top Artificial Intelligence Jobs

Embed with high-value customers to discover and deliver off-platform tasking solutions, own the end-to-end fellow experience in external environments, graduate bespoke integrations into platform primitives, define success metrics for speed and repeatability, and partner cross-functionally to scale revenue-critical AI training-data engagements.

Build, maintain, and improve production AI services for document understanding including LLM pipelines, background jobs, databases, and internal tools. Implement features, debug production issues, write tests, and contribute to evaluation frameworks to improve model quality and system reliability. Collaborate with a small engineering team and take increasing ownership over platform components.

Lead R&D data, analytics, and AI strategy to modernize platforms, governance, and insights across clinical development. Deliver benchmarking, dashboards, predictive analytics, and AI-enabled solutions to accelerate trials, improve data quality, and inform portfolio and operational decisions. Partner with enterprise IT, data engineering, and external vendors to execute roadmaps and measure business value.

Lead product marketing and content for a multi-product B2B SaaS roofing platform. Own positioning, GTM strategy, messaging, sales enablement, launch and content strategy, market intelligence, and AI-enabled workflows. Build and mentor a small team while operating hands-on to drive win rates, sales velocity, product adoption, and scalable revenue-focused marketing systems.

Lead design and implementation of Generative AI features and RAG solutions, integrate LLMs with internal systems, build APIs and workflows, mentor engineers, ensure enterprise SDLC, security, and responsible AI practices.

Define, implement, and enforce enterprise-wide engineering and operational standards for AI/ML development. Lead and mentor teams to ensure reliable, scalable, and compliant Databricks-based workflows, MLOps, CI/CD, and model lifecycle governance. Build templates, audit processes, documentation, and automated quality checks to standardize notebooks, models, and pipelines across the program.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

Design, build, and operate a secure, high-performance agent sandboxing platform. Ensure isolation, reproducibility, and efficient resource use across containerized/virtualized environments. Improve developer experience (APIs, client SDKs, docs), debug and monitor production incidents, lead architecture and end-to-end projects, and collaborate with internal teams to shape roadmap and tooling.

Lead enterprise AI transformation by discovering opportunities, designing and building scalable LLM/agent-based workflows, architecting data and integration solutions, partnering with cross-functional stakeholders, and driving production delivery and adoption across business units.

Lead practical AI adoption by designing and maintaining Claude prompts, templates, and workflows; identify process improvements and automate repetitive tasks; support digital marketing (social posts, short videos, website updates), optimize Google Business Profiles, train users, and collaborate cross-functionally to increase operational efficiency.

Ship and operate an LLM-backed dissolution support product: design retrieval, tool contracts, structured outputs, observability, and safe human handoffs; build versioned evaluations, privacy/tenant safeguards, rollout/canary plans, and iterate on production failures to improve product reliability and customer outcomes.

Design, operate, and scale the company I developer platform and core AI infrastructure. Enable internal teams to build, test, and publish AI agents; enforce AI standards and governance; manage model configurations, usage analytics, spend governance, observability, and MCP server integrations; and provide documentation, training, and support to internal builders.

Lead visual storytelling and AI adoption within Finance by creating CFO-facing presentations, demos, and reusable design systems; translate finance workflows into credible AI use cases, partner cross-functionally to scale flagship examples, and amplify Economic Research and Finance insights for internal and external audiences.

The Senior Counsel will provide legal advice, identify risks, develop strategies, and support product teams in AI-related legal matters, ensuring compliance and innovation in AI products.

Lead end-to-end technical programs to deploy OpenAI models with cloud partners. Own integrated roadmaps, launch readiness, partner engineering coordination, risk and blocker tracking, reporting to senior leaders, and build repeatable launch pipelines for model integration, deployment, and post-launch stability.

Design and implement agentic AI solutions, LLM integrations, and scalable ETL/ELT pipelines. Lead data modernization, data quality, metadata management, and governance efforts while collaborating with business, architecture, and engineering teams.

Serve as liaison between business and centralized AI teams to identify, prioritize, and implement AI and automation opportunities. Analyze workflows, manage projects end-to-end, coordinate stakeholders, and drive change management, training, and adoption to deliver measurable operational efficiencies and improved client and employee experiences.

Lead design, delivery, and scaling of production AI/ML and enterprise automation systems (LLMs, RAG, RPA) for Medicare Advantage workloads. Own data infrastructure, MLOps, integration, monitoring, compliance (HIPAA), and reliability. Mentor engineers, set engineering standards, and drive responsible AI and automation governance across the organization.

Own product vision, strategy, and roadmap for Unity AI workstreams. Partner with engineering, design, and ML to deliver agentic AI workflows, run rapid iterations driven by user feedback, define evaluations and benchmarks, conduct user and market research, and lead cross-functional alignment to drive impactful product launches.

Design, develop, and productionize ML and LLM-based systems: implement models, build and optimize data pipelines, run experiments and A/B tests, deploy and serve models (containers/APIs), ensure code quality and reproducibility, and collaborate across engineering and systems teams while maintaining security and compliance for classified environments.

Lead deployment of NVIDIA GPU and networking solutions in customer data centers: design networks and compute/storage, debug cluster performance, run POCs, advise product roadmap, and support bring-up and customer engagements.

As an AI Automation Engineer, you'll architect CI/CD workflows, ensure pipeline reliability, and enhance productivity through AI-driven automation, collaborating across teams.

Design, develop, and deploy enterprise-scale Data & AI architectures (including MDM, ML, deep learning, and generative AI) across cloud and on-prem. Define technical strategy and roadmaps, lead and mentor cross-functional teams, ensure scalable secure solutions, drive AI adoption and literacy, evaluate technologies, and translate complex technical concepts to business stakeholders.

Design and implement scalable AI/ML-driven HealthTech microservices, contribute to technical architecture, write high-quality code, perform code reviews, develop proofs-of-concept, troubleshoot production issues, and document work while collaborating in agile teams.

Direct AI coding agents to build and ship marketing sites, maintain brand voice and design discipline across five brands, own SEO and information architecture, run competitive research, and review/correct AI-generated code and copy to ensure production-quality outcomes.