Top Artificial Intelligence Jobs

Own full-cycle recruiting for Engineering and Operations roles at a fast-growing healthcare AI startup. Partner with founders and hiring managers to define roles, source and close technical talent, optimize interview processes in Ashby, use AI tools to scale recruiting, and deliver an exceptional candidate experience through onsite hosting and office tours.

Develop and deploy AI and machine learning models, applications, and data pipelines. Responsibilities include model training and optimization, production integration, API development, data preprocessing, performance monitoring, research, and collaboration with technical and business stakeholders. The role requires Python and SQL, data science experience, knowledge of mathematical modeling, and familiarity with cloud, containerization, data engineering, and AI frameworks.

Build and evolve ngrok’s AI Gateway, including real-time AI traffic handling, policy enforcement, rate limits, usage caps, access rules, token tracking, observability, and explainability. Design customer-focused gateway abstractions and collaborate with Gateway, Customer Data, Platform, product, and design teams to align enforcement, usage, and billing signals. The role involves full-stack development using primarily Go and TypeScript in a production AWS, Kubernetes, and distributed-systems environment.

Own a portfolio of 5–8 frontier AI and enterprise accounts, developing executive relationships, identifying AI security evaluation use cases, and managing opportunities through negotiation, contracting, close, and expansion. The role requires complex enterprise sales expertise, technical curiosity, disciplined qualification, account planning, value development, and collaboration with sales engineering, product, security, and professional services teams. The successful candidate will create meaningful revenue, maintain forecast accuracy, and support customer adoption.

Leads technical marketing for Delinea’s AI security business. Builds AI security demos and labs, develops technical content and best practices, enables sales engineers and partners, validates use cases with product teams, translates security research into marketing materials, reviews technical content, presents at industry events, and analyzes competing products.

Designs and supports enterprise data platforms, pipelines, models, Power BI reporting, and AI-enabled applications. Develops ETL/ELT integrations, semantic models, dashboards, data governance controls, and scalable analytics solutions. Partners with stakeholders to identify AI use cases, build proofs of concept, and deploy solutions using Python, Azure OpenAI, LLMs, RAG, and AI agents. The role also optimizes performance, establishes responsible AI practices, and supports cloud and hybrid data environments.

Leads the technical architecture and development of liquid cooling systems for AI data centers and high-density computing. Designs direct-to-chip, immersion, and two-phase cooling systems; develops system roadmaps; evaluates pumps, valves, heat exchangers, piping, and controls; translates customer needs into technical specifications; collaborates with NVIDIA and other partners; mentors engineers; and represents the company in ASHRAE and Open Compute Project standards organizations.

Designs and operationalizes IAM controls for human, non-human, and AI agent identities. Responsibilities include authentication, authorization, secrets management, lifecycle governance, least-privilege policy-as-code, identity architecture, monitoring, incident response, compliance, and integration with AI development teams. The role also builds and leads a technical team focused on agentic IAM and workload identity.

Leads the architecture, implementation, and deployment of cloud-native, AI-powered healthcare solutions. Defines scalable designs, evaluates technologies, oversees delivery, conducts architecture reviews, and ensures alignment with business and technical requirements. Partners with product, engineering, data, IT, and business stakeholders while mentoring architects and guiding modernization initiatives. Requires extensive software architecture, cloud, full-stack development, database, DevOps, AI/ML, and healthcare workflow experience.

Designs and develops AI-powered applications and workflows, integrates AI with Azure and enterprise platforms such as Microsoft Dynamics 365, and optimizes solutions for performance, scalability, and security. Collaborates with business teams to identify automation opportunities, supports analytics initiatives, monitors emerging AI technologies, and trains users on Microsoft Copilot and related tools.

Leads the design, development, deployment, and operation of production ML, LLM, and generative AI systems. Sets technical direction, establishes engineering and MLOps standards, mentors engineers, evaluates and fine-tunes generative models for NLP, and implements monitoring for reliability. Partners with technical and business stakeholders to prioritize use cases and communicate AI capabilities. Develops scalable pipelines, microservices, and model lifecycle governance while applying current AI research and external APIs.

Own the strategy and outcomes for Mural’s AI Workflows products from concept through launch. Build prototypes, identify valuable collaboration workflows, define product-quality evaluations, guide model and routing decisions, and determine whether to iterate, scale, or stop based on user evidence. Partner with engineering, ML, design, finance, growth, and other teams on reliability, unit economics, data loops, and agentic multiplayer experiences.

Lead AI-driven software engineering and enterprise platform modernization. Build full-stack applications using TypeScript, Node.js, NestJS, React, and Python; integrate LLMs, generative AI, workflow automation, and intelligent decisioning; modernize legacy systems into cloud-native architectures; and establish AI-assisted development, governance, testing, security, and engineering best practices across AWS, Azure, and GCP.

Lead the architecture, development, deployment, and continuous improvement of enterprise AI and machine learning systems. Design generative AI, RAG, agentic, orchestration, MLOps, evaluation, monitoring, and security patterns. Build AI-enabled workflows integrating enterprise data, tools, and model APIs. Partner with executives, engineering, data science, security, and business teams to convert ambiguous problems into scalable solutions and measurable outcomes. Provide technical leadership, mentorship, reusable components, and responsible AI controls.

Build and deploy AI-powered tools, workflow automations, integrations, prototypes, and business solutions for Wiley Research. Translate ambiguous operational problems into technical requirements, develop LLM and automation solutions, integrate Microsoft 365 and enterprise systems, and collaborate with nontechnical stakeholders. The role also evaluates AI platforms, supports sprint delivery, ensures security and governance compliance, documents architectures, and improves development practices.

Own the architecture and production reliability of Ande’s AI agent system. Build agent tools, safety controls, evaluation suites, venue correspondence workflows, memory, supply-learning systems, and typed classifiers. Improve latency, failover, quality, and safe handling of real-money actions while mentoring engineers and defining AI-native development practices. The role requires strong TypeScript and backend systems expertise, production LLM agent experience, and judgment about deterministic versus model-driven behavior.

Lead enterprise portfolio management and change adoption for employee-impacting initiatives. The role establishes governance, prioritization, reporting, dependency and risk visibility, and coordinates technology, operations, communications, training, and executive stakeholders. It translates complex changes into actionable communications, identifies adoption barriers, supports implementation and reinforcement, and may eventually manage portfolio operations or initiative delivery staff. The role also applies AI to improve decision-making and operational efficiency with appropriate governance and oversight.

Own the technical sales motion for enterprise AI solutions, including discovery, architecture, demonstrations, proof-of-concept engagements, and executive-level technical narratives. Design RAG and generative AI solutions integrated with Cohesity’s data security platform, lead customer workshops, collaborate with hyperscalers and NVIDIA, and provide field feedback to Product and Engineering. The role requires strong AI, infrastructure, cloud, security, and pre-sales expertise.

Builds and operates reliable, governed data pipelines and AI platform services on Databricks and Azure. Responsibilities include batch and streaming ingestion, transformation, orchestration, feature and retrieval pipelines, evaluation data, model and agent monitoring, MLOps and LLMOps infrastructure, CI/CD, infrastructure as code, observability, incident response, and cost optimization. The role also mentors engineers, supports AI agent implementations, maintains semantic layers, and partners with architecture and analytics teams to deliver dependable data products.

Designs trusted data products and end-to-end AI solutions for analytics, predictive AI, generative AI, agents, and automation. Responsibilities include data modeling, contracts, semantic and context layers, Databricks governance, MLOps and LLMOps, evaluation, monitoring, security, privacy, and operational readiness. Partners with engineers, operators, and application teams to prototype, deploy, support, and standardize reusable solutions across mobility and other business domains.

Build and deploy production AI and GenAI applications, including RAG, conversational AI, agentic workflows, and intelligent automation. Develop scalable backend services, APIs, cloud-native workloads, CI/CD pipelines, infrastructure-as-code, and MLOps/LLMOps processes. Design secure networking, optimize performance and costs, implement monitoring and lifecycle management, and collaborate with stakeholders to meet governance, security, and audit requirements. The role may lead teams or collaborative assignments and influence enterprise-wide decision-making.

Lead the architecture and delivery of secure, scalable AI, machine learning, data, and analytics solutions for Finance on AWS. Translate business needs into production-ready systems, establish data and AI governance, apply responsible-AI and model-lifecycle practices, protect financial controls and information, guide technical teams, and optimize solution reliability, cost, and performance.

Serves as the on-site engineer for installing, administering, securing, and authorizing EdgeRunner AI software across DoD networks. Coordinates RMF, STIG, ACAS, SCAP, and eMASS activities; manages hosts, accounts, patches, backups, and compliance evidence. Works with JOC personnel to identify automation opportunities, build AI agents and simple web views, prepare operational data, train users, and support system handoff. Classified-network support may include SIPR and JWICS installations.

Architect, deploy, operate, and monitor on-premises LLM infrastructure and agentic workflows. Build RAG pipelines, MCP servers, data pipelines, CI/CD integrations, testing environments, tracing and evaluation tools, and model-serving network requirements. Support Kubernetes-based AI/ML infrastructure and software development environments while collaborating with cross-functional teams. The role requires a bachelor's degree with four years of related experience or a master's degree with two years, plus TS/SCI clearance.

Supports data-driven irrigation optimization by extracting and cleaning Azure data, performing exploratory analysis, engineering features, developing machine learning models, identifying data quality issues, documenting findings, and collaborating with engineering and product teams. The internship also involves presenting insights and potentially integrating data solutions into production environments.