Top Artificial Intelligence Jobs

Provides executive oversight of end-to-end healthcare revenue cycle operations, including patient access, coding, billing, denials, collections, payer contracting, and financial performance. Evaluates AI-generated analyses and workflow tools, establishes enterprise KPIs, leads transformation initiatives, and presents strategic recommendations to executives, boards, and investors. The role also annotates AI outputs and provides structured feedback for healthcare AI training datasets.

Partners with sales teams and customers to drive AI and Developer Platform revenue. Leads technical discovery, architects cloud and serverless solutions, develops proofs of concept, presents to strategic stakeholders, and advises customers on Cloudflare products. Collaborates with product, marketing, and technical teams to improve adoption, content, use cases, and value propositions.

Develop AI harnesses and agentic systems integrating AI/ML services, enterprise platforms, cloud infrastructure, and modern user interfaces. Build secure, distributed architectures; implement CI/CD, automated testing, performance and token optimization, and anti-hallucination controls. Collaborate with technical and executive stakeholders to deliver strategic systems, support rapid-cycle R&D, and improve engineering standards. The onsite McLean role includes occasional travel and requires eligibility for a security clearance, with TS/SCI preferred.

Lead the technical and product direction for Klaviyo’s Customer Agent, building and scaling production conversational AI systems. Design orchestration, evaluation, guardrails, memory, context, and failure-handling capabilities; write and review code; resolve complex production issues; establish reusable engineering patterns; mentor engineers; and partner with product, design, data science, and go-to-market teams. The role requires hands-on ownership of reliable, secure, multi-tenant agentic systems operating against live customer traffic.

Leads SEO and Answer Engine Optimization strategies across traditional search engines and generative AI platforms. Oversees content workflows, optimizes metadata, landing pages, site architecture, crawlability, redirects, canonicalization, and crawler directives, and partners with web development teams. Uses enterprise SEO, AI observability, and analytics tools for keyword research, competitive intelligence, performance reporting, and growth opportunities. Also guides content teams and independently manages complex initiatives.

Build the data and AI platform for construction equipment dealerships. Responsibilities include developing Python ingestion pipelines, orchestrating cloud workloads, maintaining canonical schemas in Databricks and PostgreSQL, resolving entities, extracting information from documents with LLMs, and building LangChain/LangGraph agent workflows. The role also involves production cloud deployment, human-in-the-loop approvals, model routing, observability, action logging, and autonomous execution safeguards.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

Lead scientific communications and external engagement for Click's digital therapeutics pipeline; publish and present clinical evidence; build KOL relationships; provide scientific support for business development and partnerships; and collaborate cross-functionally with product, clinical, and regulatory teams to align development with clinical evidence and market access requirements.

Owns customer success for strategic enterprise customers using Edwin’s AI platform. The role guides customers from deployment through adoption, value realization, and Autonomous IT maturity; develops success plans, monitors usage and health signals, leads executive business reviews, demonstrates ROI, identifies expansion opportunities, and coordinates with Sales, Product, Engineering, Marketing, and AI Impact teams. The strategist also contributes customer insights and improves AI-first engagement playbooks.

Lead design, development, and deployment of scalable AI/ML applications and MLOps pipelines on Kubernetes. Mentor engineers, define best practices, adopt emerging GenAI tools, ensure observability, security, and alignment with business objectives, and maintain documentation and runbooks.

Serves as a technical authority and trusted advisor for sales teams, customers, and partners. The role develops customer architectures, demonstrates Elastic solutions, supports sales cycles from discovery through technical win, enables partners, creates sales collateral, engages communities through events, and communicates customer needs to product and engineering teams. The position requires strong technical presales and customer relationship skills, with 30% regional travel and occasional international travel.

Lead and grow a team of approximately 12 kernel engineers developing high-performance inference kernels across NVIDIA, AMD, and ASIC hardware. Own the MAX kernel roadmap, performance optimization, portable kernel architecture, and delivery. Partner with framework, serving, compiler/runtime, hardware enablement, and Qualcomm teams while advancing AI-assisted development, verification, and engineering practices.

Own the vision, roadmap, and lifecycle of Kong’s AI Management portfolio, including AI Gateway capabilities, agentic harnesses, LLM routing, policies, context, guardrails, and evaluation. Align engineering, design, marketing, and executives; translate complex AI infrastructure concepts into product requirements; manage backlogs, dependencies, technical debt, and delivery timelines; and support customer engagements and sales enablement.

Designs, optimizes, validates, and supports AI accelerator systems, GPU clusters, Gaudi platforms, and cloud infrastructure for large-scale machine learning workloads. Responsibilities include platform bring-up, system-level debugging, test planning, firmware integration, automated testing, monitoring, full-stack troubleshooting, production deployment, architectural improvements, and mentoring junior engineers. The role collaborates with OEM vendors and cross-functional teams to improve AI infrastructure performance, stability, and reliability.

Architects and builds AI-augmented DataOps capabilities for resilient, observable, scalable data platforms. Responsibilities include intelligent pipeline failure detection and recovery, AI-driven data quality monitoring, automated remediation, productionizing agentic systems, leading architecture and design reviews, partnering across engineering and business teams, troubleshooting platform performance, evaluating modern data and AI technologies, and mentoring engineers.

Develops and implements AI and LLM solutions, including generative AI applications, pilot projects, deployment, monitoring, troubleshooting, testing, and integration. Collaborates with technical and functional stakeholders to identify use cases, define requirements, evaluate models, incorporate feedback, and transition successful pilots into operational capabilities.

Lead an agile engineering team to architect, develop, and productionize secure, scalable software that integrates LLMs and generative AI. Drive AI-assisted engineering practices, mentor engineers, translate business requirements into technical roadmaps, automate reliability, and ensure compliant, high-quality delivery.

Build, deploy, and operate AI/ML models, scalable MLOps pipelines, and real-time inference systems. Collect and process diverse data sources, improve training data quality, monitor model reliability and drift, and validate AI features with real-world users. The role also involves documenting best practices, mentoring teammates, and staying current with emerging AI technologies, LLMs, security risks, and model-development techniques.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Design, develop, deploy, and scale AI-powered solutions, digital workers, intelligent automation, and enterprise AI capabilities. Build Python-based generative and agentic AI solutions using LLMs, vector databases, embeddings, semantic search, and knowledge retrieval. Integrate AI with enterprise platforms and workflows, establish engineering and responsible AI standards, evaluate emerging technologies, and lead innovation pilots in collaboration with business, architecture, engineering, and operations teams.

Leads AI/ML engineering teams and technical strategy for enterprise-scale healthcare and pharmacy solutions. Oversees development, deployment, governance, MLOps, security, and operations for generative AI, LLM, NLP, predictive analytics, and agentic AI capabilities. Partners with product, engineering, data science, and executives to align technology with business goals, evaluate emerging technologies, improve automation, and develop engineering talent.

Leads the design, development, and deployment of AI-powered solutions, intelligent automation, and digital workers. Prototypes and scales generative and agentic AI capabilities, integrates them with enterprise platforms and workflows, and establishes AI engineering, governance, security, privacy, and responsible AI standards. The role also leads technical workstreams, mentors engineers, evaluates emerging technologies, and drives enterprise adoption of innovative AI solutions.

Own AI product roadmaps for finance and enterprise functions, translating business priorities into agent requirements, user stories, and deployment plans. Lead process discovery, redesign, sprint planning, stakeholder alignment, change management, training, governance, and ROI measurement. Partner with engineers and data architects to deliver production AI solutions, present results to executive stakeholders, support client growth initiatives, and mentor delivery team members.

Own architecture and direction for Suno’s AI Operations Engineering team. Build intelligent agents, workflows, internal tools, APIs, authentication patterns, and self-service deployment capabilities that help non-engineering teams scale. Evaluate third-party tools, integrate customer experience systems such as Zendesk, and partner with legal, finance, people, support, and content operations to identify and solve workflow challenges.

Build AI agents, workflows, internal tools, APIs, authentication patterns, and customer experience integrations that improve operations across support, legal, finance, people, and content teams. Evaluate build-versus-buy decisions, support self-service app deployment, and partner with stakeholders to identify automation opportunities. The role requires full-stack engineering experience and comfort working in an early-stage, rapidly evolving environment.