Top Artificial Intelligence Jobs

Lead end-to-end delivery of production AI solutions (ML, GenAI, agentic systems, RAG) on the enterprise AI platform. Design, prototype, deploy, monitor, and evaluate solutions; set technical standards; mentor engineers; and partner with stakeholders to drive measurable business value and adoption.

Develop and productionize machine learning, deep learning, NLP, LLM, and generative AI solutions. Conduct experiments, tune and evaluate models, optimize accuracy and performance, and integrate AI capabilities into a multi-agent platform. Write end-to-end Python code for proofs of concept and production systems while collaborating with product, engineering, and business teams. Preferred work includes distributed training, search and ranking, recommender systems, cloud deployment, and large-scale MLOps.

Own the strategy, roadmap, reliability, governance, security, privacy, compliance, and lifecycle management of an enterprise AI platform supporting search, assistants, agents, and workflow orchestration. Manage backlogs, define requirements and service metrics, oversee operational readiness, coordinate engineering and business stakeholders, and drive continuous improvement. Enable platform adoption while promoting responsible AI, resilience, user trust, and effective governance across a complex global enterprise.

Conduct foundation-model research for robotic manipulation and high-precision manufacturing. Responsibilities include data curation, simulation, model training, evaluation, rigorous testing on industrial robots, and deployment to production hardware. The intern will work independently on ambiguous AI problems, develop multimodal models, use reinforcement or imitation learning, collaborate cross-functionally, and write maintainable production-quality R&D code.

Own and improve catalog data completeness, analyze gaps and root causes, champion AI and automation to increase quality and scale, partner with technical teams to drive systems improvements, create SOPs and governance, communicate insights to stakeholders, and mentor junior team members.

Develop physics-informed AI surrogates and surrogate modeling workflows for CFD, FEA, thermal and multi-physics simulations. Create scripted simulation models to generate training data, research new physics-AI methods (PINNs, neural operators, digital twins), integrate surrogates into engineering design, support government and internal R&D projects, and collaborate with cross-functional teams to transition models into engineering workflows.

Design, develop, and operate a production agentic data platform using Spark, Kafka, Airflow, Snowflake and AWS. Write secure high-quality code across frontend and backend, leverage enterprise AI-assisted development and LLMs (Claude Code, agents, RAG, embeddings), build autonomous agents and MCP server components, apply secure/responsible AI practices, troubleshoot systems, and own full-stack application/platform delivery with CI/CD, containers, and observability.

Lead an agile engineering team to design, build, and operate high-scale, secure Java applications and internal developer platforms. Drive adoption of enterprise-authorized AI-assisted development tools, improve observability and reliability (SLOs, alerting), own on-call and incident response, and mentor peers on responsible AI usage and best engineering practices.

As the Principal Product Designer for Toast IQ, you'll lead design strategy for the AI assistant, shaping user experiences across platforms and collaborating with cross-functional teams.

Design and deliver AI/ML solutions for Business Banking, including AI-driven validation, campaign quality checks, recommendation models, trend analysis, and automated quality metrics. Integrate AI tools with enterprise systems, collaborate with Marketing, Legal, Compliance, and Controls, and provide executive-ready insights. The role emphasizes Generative AI, agents, NLP, LLMs, machine learning, cloud deployment, customer experience, and continuous improvement of campaign validation processes.

Leads complex, cross-functional Enterprise Technology, AI, and Data programs from planning through delivery. Responsibilities include roadmap development, dependency and resource management, risk and issue tracking, governance, executive communications, operational transformation, enterprise application initiatives, and AI workflow adoption. The role aligns technical and business stakeholders across Finance, Legal, Sales, Marketing, Product, and other functions while improving program delivery practices in a remote, globally distributed environment.

Build agentic AI workflows that automate Finance processes, including close, reconciliation, forecasting, and reporting. Develop telemetry for AI usage, cost, engagement, and ROI; help design user experiences; validate explainable and auditable outputs; and prototype solutions for production with platform teams. The role also requires learning Finance processes and applying SQL rigor while collaborating with technical and non-technical stakeholders.

Lead design and deliver multi-cloud AI/ML solutions that ingest and correlate telemetry (metrics, logs, traces), build predictive anomaly detection and forecasting models, automate closed-loop remediation, integrate with ticketing systems, and provide self-service or conversational interfaces to reduce MTTR and ticket volume.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps capabilities, CI/CD pipelines, infrastructure automation, observability, monitoring, governance, and AI deployment services. Support production machine learning and generative AI workloads, troubleshoot incidents, improve reliability and performance, collaborate with engineering, security, and governance teams, evaluate emerging technologies, and mentor peers.

Lead cross-functional cybersecurity programs to reduce enterprise risk from frontier AI models. Drive planning through execution, prioritize vulnerabilities, expand WAF coverage, enable AI-driven testing and automated patching, strengthen identity controls and telemetry, escalate risks, provide executive reporting, and coordinate remediation across technology, product, operations, and business stakeholders.

Own the strategy, configuration, and continuous improvement of HoneyBook’s support chatbot and related Help Center and internal documentation. Analyze resolution data, identify content gaps, prioritize roadmap investments, configure Intercom, maintain data connectors, evaluate AI tools, and build internal low-code applications. Create clear technical documentation, improve AI-assisted workflows, maintain governance, and collaborate with Member Care, Product, QA, Marketing, and other teams to improve member and support specialist experiences.

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.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps, infrastructure automation, CI/CD, observability, governance, deployment, monitoring, and lifecycle-management capabilities for machine learning and generative AI workloads. Support production platforms, troubleshoot incidents, improve reliability and performance, collaborate with engineering, security, and governance teams, evaluate emerging technologies, and mentor peers.

Design, implement, and deliver AI-enabled product features (LLMs, agents, ML). Build cloud-native services, ensure observability, reliability and scalability, troubleshoot production issues, participate in architecture reviews, and collaborate with product, data, and engineering teams.

The Staff Forward Deployed Engineer at GitLab works with strategic accounts to adopt GitLab and GitLab Duo Agent Platform, guiding technical discovery and building reusable solutions for complex enterprise environments.

Leads a Sales Innovation Studio focused on AI-enabled sales experiences, rapid prototyping, concept validation, and organizational capability building. Develops sales experience strategy, manages an innovation portfolio, enables responsible AI adoption, coaches teams, and partners with Product, Technology, Design, Research, Compliance, and Enterprise AI. Establishes governance, reusable tools, transition criteria, and executive-level reporting to move validated concepts into scalable business capabilities.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps, automation, CI/CD, infrastructure, observability, monitoring, governance, and deployment capabilities for machine learning and generative AI workloads. Support production systems, incident response, troubleshooting, reliability, and lifecycle management. Collaborate with engineering, infrastructure, security, and governance teams, evaluate emerging technologies, contribute to architecture and strategy, and mentor peers.

Design, develop, validate, and deploy GenAI and traditional machine learning solutions for healthcare operations. Build scalable Python inference APIs on Kubernetes, implement semantic search, develop LLM and agentic applications, and maintain AI/ML pipelines with monitoring, documentation, and coding standards. Evaluate emerging technologies, promote responsible AI, and use AI-assisted development tools to improve software delivery. The role is fully telecommute within the United States.

Lead AI alignment, roadmap, and execution for Operations & Service Delivery—prioritizing AI use cases, driving workflow transformation, governance, metrics, cross-functional delivery, and building AI operational capability and teams to realize business outcomes.