Top AI Engineer Jobs

Design and build production agentic AI systems and internal automation workflows across business functions. Responsibilities include prioritizing high-impact automation, developing multi-step and multi-agent workflows, integrating agent platforms, implementing human-in-the-loop controls, improving reliability and observability, evaluating performance, and shaping reusable AI engineering patterns. The role requires collaboration with nontechnical stakeholders and ownership of ambiguous problems from definition through measurable business impact.

Build and deploy enterprise AI solutions using Claude, LLMs, RAG, agentic workflows, tool use, and structured outputs. Integrate AI applications with ServiceNow and enterprise systems, develop secure APIs and supporting services, evaluate model quality, implement responsible AI safeguards, troubleshoot production workflows, and collaborate directly with customers. The role also contributes reusable accelerators, platform capabilities, documentation, and deployment best practices while supporting discovery, prototyping, implementation, and production rollouts.

Deploys enterprise AI and agentic workflows for customers using Claude, LLM integrations, RAG, tool use, structured outputs, and responsible AI controls. Builds full-stack integrations across ServiceNow and enterprise systems, develops prototypes and production solutions, evaluates AI quality and safety, troubleshoots deployments, and contributes reusable components to NewRocket’s AI platforms. The role is highly customer-facing and requires approximately 70% travel.

Deploy and operationalize enterprise AI workflows, agents, RAG systems, and integrations across ServiceNow and connected platforms. Build full-stack components, LLM applications, APIs, prototypes, and production solutions using Claude and other AI technologies. Collaborate with customers and internal teams on discovery, implementation, troubleshooting, evaluation, security, responsible AI, and continuous improvement. Develop reusable accelerators, platform capabilities, integration patterns, and deployment practices while traveling extensively to support customer engagements.

Build and deploy secure, production-ready generative AI solutions for enterprise customers. Responsibilities include developing Claude-powered agents, RAG systems, workflow automations, APIs, integrations, and full-stack components; conducting evaluations; implementing responsible AI controls; troubleshooting deployments; and collaborating with customers and internal teams. The role also contributes reusable accelerators and platforms while requiring substantial customer engagement and travel.

Design, train, and deploy reinforcement and imitation learning policies for humanoid robots. Run physical robot experiments, improve sim-to-real transfer, maintain simulation environments and data pipelines, analyze deployment failures, and collaborate with hardware and firmware teams to improve policy robustness.

Lead the architecture and delivery of scalable AI-powered, cloud-native products and full-stack platforms. Define standards for agentic workflows, LLM systems, RAG, data grounding, AI evaluation, governance, security, and observability. Guide multiple engineering squads, establish technical roadmaps, oversee architectural decisions, mentor teams, and partner with executives and product stakeholders. Build and govern production systems using modern frontend, backend, cloud, data, and DevOps technologies.

Leads AI and data engineering for private equity platforms, building production LLM applications, MCP services, agents, and governed data products. Manages contract engineers while remaining hands-on with coding, architecture, evaluation, security, data quality, CI/CD, and production reliability. Partners with investment, finance, compliance, and business teams to convert ambiguous workflows into reliable software and ensures accurate, auditable portfolio, valuation, fund performance, and investor reporting data.

Build and lead production-grade agentic AI systems for enterprise clients across AWS, OCI, and Azure. Own full-stack development, cloud deployment, CI/CD, LLM integrations, agent orchestration, memory and retrieval architectures, APIs, identity, security, and operational documentation. Engage directly with stakeholders, translate requirements into scalable systems, advise on model and cloud strategy, and help productize the company’s delivery approach.

Own and improve ClickUp’s voice AI systems, including real-time speech-to-text, audio processing, context-aware transcription, LLM post-processing, mention resolution, formatting, and voice-to-action workflows. Evaluate and integrate ASR models for accuracy, latency, and cost, while collaborating across product and platform teams to deliver voice and multimodal assistant features across desktop, mobile, web, and browser extension products.

Own and scale production LLM features and browser automation for a consumer subscription product. Responsibilities include prompt systems, structured outputs, Playwright and Browserbase automation, workflow orchestration, evaluation pipelines, observability, model experimentation, cost and reliability optimization, and production incident ownership. The role uses a modern TypeScript stack with Cloudflare Workers, Next.js, Postgres, Hatchet, and related AI tooling.

Design and build production-grade agentic AI systems and RAG pipelines, partner with business leaders to automate complex workflows, prototype across AI platforms, integrate models, APIs and data sources, and drive enterprise adoption and scalable solutions.

Build, deploy, and operate production AI systems including LLM applications, RAG pipelines, agents, APIs, data workflows, and containerized services. Implement governance, security, observability, evaluation, and cost controls for enterprise AI. Collaborate across engineering, product, data science, compliance, and business teams; support architecture decisions, incident response, CI/CD, and mentoring. The role focuses on reliable AI capabilities for insurance underwriting, claims, member experience, and risk management.

Build and lead Kroll’s CrowdStrike Falcon AIDR deployment practice. Personally deploy, configure, integrate, and tune AIDR across client environments; connect it with Falcon SIEM, SOAR, Cloud Security, Data Protection, and Identity Protection; develop detection and data-protection policies; validate AI security controls; create runbooks and automation workflows; advise clients on deployment architecture; support pre-sales; and mentor consultants.

Design and implement AI-powered image and video generation workflows for creative production. Collaborate with artists to integrate open-source generative models, 3D inputs, fine-tuned diffusion models, LoRAs, and ComfyUI features. Develop custom nodes and Python utilities for data processing and workflow automation while translating AI research into practical applications for film, commercial, and immersive experiences.

Build and operate production AI agent systems using Python, LangChain, LangGraph, and Amazon AgentCore. Develop agent tools, Snowflake integrations, retrieval workflows, structured outputs, evaluations, guardrails, and observability. Deploy, monitor, and improve reliable LLM-powered features while collaborating with data engineers. The role requires production Python experience, hands-on LLM development, evaluation framework design, and familiarity with modern AI tooling. Periodic travel may be required.

Design and develop secure, scalable Salesforce AI solutions, including Agentforce agents, multi-agent orchestration frameworks, custom MCP servers, and AI-client integrations. Build integrations using APIs and platform events, implement observability and security controls, translate business requirements into technical specifications, and support AI platform users. The role requires extensive Salesforce ecosystem experience, expertise with Data 360 and Headless 360, and hands-on Python or JavaScript development for AI and MCP services.

Design, build, and deploy enterprise-grade Generative AI and agentic AI platforms. Responsibilities include developing LLM, RAG, Graph RAG, vector search, multi-agent, MCP, data, backend, full-stack, cloud, inference, observability, LLMOps, security, and governance solutions. The role requires production deployment across AWS, Azure, and GCP, optimization for accuracy, latency, cost, scalability, and reliability, and collaboration with cross-functional technical and business teams.

Designs, deploys, and secures AI agents for enterprise risk and security operations. The role manages AI governance, Microsoft security and compliance controls, data protection, DLP, insider risk, access controls, monitoring, incident response, and SIEM/XDR integrations. It also leads security architecture, automation, AI risk evaluation, cross-functional initiatives, and mentoring while supporting secure adoption of Microsoft Copilot and Azure AI platforms.

Develop production-grade AI agents and reasoning workflows in Python, including memory management, sub-agent delegation, MCP server integrations, resilient execution pipelines, guardrails, testing, observability, and enterprise system connectivity. Collaborate with business and technology teams, coach junior engineers, review code, influence architecture, and ensure compliance, security, and quality standards across agile delivery squads.

Design and build production-grade AI agent and RAG systems for enterprise network automation. Responsibilities include agent orchestration, tool execution, memory, safety controls, LLM fine-tuning, evaluation, observability, reliability engineering, performance optimization, and production operations. The role also prototypes emerging AI technologies, diagnoses complex system failures, and collaborates across Engineering, Product, and QA to deliver scalable, secure AI capabilities.

Build and operate production-grade, cloud-native AI applications across the full stack. Responsibilities include integrating and optimizing AI models, developing evaluation and monitoring pipelines, operating reliable production systems, collaborating with stakeholders, leading design reviews, mentoring engineers, and participating in on-call rotations. The role supports proof-of-concepts, MVPs, and long-term product development while applying MLOps, LLMOps, DevOps, security, testing, and SRE practices.

Design, build, and deploy production-grade AI systems focused on Generative AI and LLM applications. Develop RAG pipelines, fine-tune models, build scalable APIs, optimize inference, and implement vector search, observability, guardrails, and responsible AI practices. Contribute to MLOps, cloud deployment, model evaluation, and reliable enterprise AI solutions while partnering cross-functionally on industrial use cases.

Build and productionize physical AI systems involving agentic AI, machine learning, reasoning, knowledge graphs, multimodal data, forecasting, optimization, and autonomous decision-making. Develop models, prototypes, and scalable software while collaborating with researchers, product teams, and engineers. The role emphasizes experimentation, reliable deployment, and solving real-world industrial problems using data from sensors, documents, images, video, and enterprise systems.

Design, build, deploy, and own production AI systems for industrial customers. Engineers implement computational knowledge graphs, replace static workflows with autonomous real-time applications, collaborate directly with operators and stakeholders, deploy systems into high-stakes operational environments, and feed field learnings into platform evolution.