Top AI Engineer Jobs

Build AI agent discovery across Azure, endpoints, and future AWS and Google Cloud environments. Assess agent configurations, access, and security risks, then integrate findings with Delinea’s identity and attribution platform for governance. Extend existing discovery services, collaborate with platform teams, and solve complex backend engineering problems using cloud APIs and modern backend languages.

Build full-stack AI agents and product features for commercial workflows, including quoting and pricing. Develop user interfaces, backend integrations, ERP connections, and privacy-compliant deployments. Work with TypeScript, React, and potentially Next.js while incorporating customer feedback and experimenting with new AI tools and agent orchestration frameworks. The role requires shipping customer-facing products and working onsite five days per week in San Francisco.

Build and operate Python backend services, APIs, and React frontends for a property-management platform. Use AI coding tools extensively for planning, implementation, reviews, testing, and evaluation. Own features end to end, integrate third-party platforms, maintain CI/CD and observability, support safe AWS releases, and collaborate with product and operations. Mentor teammates through code reviews and help establish effective AI-assisted engineering practices.

Lead enterprise GenAI platform architecture and strategy; design and own agentic LLM systems at scale; drive company-wide LLM integration and cognitive automation roadmap; set engineering standards for distributed ML systems; align cross-functional and executive stakeholders; mentor and hire senior engineering talent.

Leads the design and development of scalable SaaS applications, including AI-powered features such as semantic search, vector databases, RAG, and LLM orchestration. Provides technical leadership, mentorship, architectural guidance, code reviews, and engineering standards enforcement. Collaborates cross-functionally to translate requirements into robust solutions, champions AI-assisted development tools, builds developer experience tooling, and drives innovation, quality, security, and continuous improvement across the engineering organization.

Conduct research and develop AI solutions for Duolingo’s education products. The role involves solving complex problems, prototyping and evaluating approaches, implementing effective solutions at scale, collaborating cross-functionally, and sharing findings internally and with the broader research community.

Design, build, and maintain production-grade AI-powered go-to-market automations (prospecting, lead scoring, campaign orchestration, attribution). Integrate ML/LLM tools into marketing and sales workflows, track business-critical metrics, ensure reliability and scalability, and collaborate with GTM strategists and non-technical stakeholders to drive measurable pipeline and revenue outcomes.

Leads teams designing and deploying AI-driven enterprise and cloud security solutions. Responsibilities include developing machine learning systems, integrating data infrastructure and pipelines, performing advanced modeling and analysis, and building AI applications with Python, Java, and C++. The role involves client engagement, strategic problem-solving, stakeholder validation, coaching, process innovation, and operational excellence while addressing complex cybersecurity and privacy challenges.

Lead and manage a software engineering team to design, build, test, and deploy AI-enabled applications (LLM integrations, RAG, APIs). Own project planning, budgeting, CI/CD, code quality, observability, microservices, and cross-functional collaboration while coaching and developing team members.

As a Founding Engineer, you'll manage the lifecycle of AI systems, optimizing performance and developing pipelines for biopharma marketing.

As a Founding AI Design Engineer, you will design visual assets and collaborate with engineers to create a platform that integrates AI with design workflows in pharma marketing.

Design and build the distributed systems layer powering a spatial computing platform: real-time coordination, persistent world state, APIs, AI platform infrastructure (vector search, retrieval, agent orchestration), and scalable services to support many concurrent users and third-party developers.

Lead Unity architecture and build persistent, multi-user spatial systems for mobile and wearables. Design spatial persistence, synchronization, AI-assisted content pipelines, and define long-term platform direction as a founding technical contributor.

Develop and deploy full-stack AI applications for healthcare, integrating LLMs, voice, image, speech, RAG, vector databases, and AI agents. Build data pipelines, optimize model performance and costs, fine-tune GPT and Llama models, create copilots and autonomous workflows, and collaborate with technical and business stakeholders. The role requires Python and Node.js development, cloud experience, MLOps knowledge, and proficiency with Git, Docker, and Kubernetes.

Designs, develops, and deploys enterprise applications using Java, Spring Boot, microservices, REST APIs, cloud services, databases, DevOps tools, and Angular or React. The role also involves AI exposure with Python, LangChain, and LangGraph, along with application security, monitoring, performance, deployment, and collaboration with project teams. The position is onsite in McLean, Virginia.

Owns end-to-end AI infrastructure deployments, including data center assessments, solution design reviews, partner coordination, technical delivery, and production operations. Responsibilities include monitoring, incident response, capacity checks, firmware and lifecycle management, hardware maintenance coordination, operational documentation, project risk reporting, and mentoring junior engineers. The role requires expertise in high-performance compute, networking, storage, Linux, cluster orchestration, automation, and observability across large-scale AI environments.

As a Senior AI Engineer, you'll develop ML and Generative AI models, ensure integration with enterprise applications, and manage large datasets while collaborating with teams and mentoring others.

Lead design and deliver cloud-native microservices and AI/LLM-driven features. Architect scalable, secure systems, drive AI-assisted engineering practices, improve observability/automation, mentor teams, and integrate generative AI into business workflows.

Design, build, and maintain AI agents and automated marketing workflows. Map automation opportunities, deploy and monitor agents, integrate with marketing tools, produce impact reporting, ensure data governance, and document/enable marketing teams.

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Lead the architecture and technical vision for production-grade agentic AI applications. Design multi-agent orchestration, memory systems, reasoning pipelines, reliability infrastructure, observability, and high-availability distributed systems. Establish engineering standards, mentor senior engineers, conduct architecture reviews, support hiring, and collaborate with Product, Research, Platform, and AI Ops teams to scale LLM-powered workloads.

Build and operate production search, ranking, recommendation, and multimodal AI systems for a global jewellery marketplace. Own machine-learning models end to end, including feature development, training, evaluation, deployment, monitoring, and low-latency serving. Develop contextual and conversational search, image-based retrieval, learning-to-rank systems, and embedding pipelines. Define ranking experiments, evaluate LLM behavior, and design graceful recovery from probabilistic failures across multi-tenant storefronts.

Administer and maintain enterprise AI platforms across development and production environments. Monitor connectors, integrations, authentication, usage, costs, and platform health; troubleshoot incidents; coordinate migrations, testing, deployments, and root-cause analysis. Maintain operational documentation and compliance records, support users and citizen developers, enable governed AI features, and improve reliability and operational processes.

Designs, develops, and operates enterprise-grade platforms for AI agents and Generative AI applications. Responsibilities include agent orchestration, LLM and AI service integration, secure tool and API invocation, prompt lifecycle management, governance and safety controls, observability, model evaluation, deployment, troubleshooting, and platform optimization. The role collaborates with engineering, security, product, and data teams to deliver scalable, reliable, and compliant AI solutions.

Build and deploy internal AI tools, LLM applications, and agentic systems from concept through production. Establish reusable engineering patterns, optimize models for cost, latency, and reliability, and create shared infrastructure. Partner with technical teams to deliver practical tools, mentor teammates, and drive autonomous end-to-end execution with strong testing, documentation, evaluation, and operational practices.