Top AI Engineer Jobs

Hands-on founding engineer building a production Shopify app, storefront widget, video upload flow, and AI extraction pipeline. Ship, harden, and operate full-stack systems, ensure reliability and conversion-focused UX, and scale tooling for self-serve merchant onboarding.

Design, build, and operate scalable integrations and AI agents using Celigo integrator.io, LangChain/LangGraph and modern cloud data pipelines. Collaborate with business stakeholders to automate processes, ensure data quality and security, own integrations end-to-end, produce technical documentation, and support operational hand-offs.

Design, test, and document prompts and prompt libraries to optimize AI language model outputs. Collaborate with product, data, and ML engineers to integrate prompt solutions, run A/B experiments, measure performance, and maintain versioned documentation. Support TIBCO-specific implementation and stay current on AI/NLP techniques.

Build and operate an on-prem AI platform (Model-as-a-Service) including RAG pipelines, vector DBs, MCP tooling, agent repositories, CI/CD integration, and guardrails within MRM. Optimize large on-prem GPU infrastructure and translate business use cases into scalable platform services.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Operate and monitor ML/AI models and agentic systems in production. Build AI observability, logging, tracing, and evaluation pipelines. Monitor LLM outputs, detect drift and model degradation, and maintain data/feature pipelines. Develop CI/CD, model versioning, experiment tracking, and automate alerts and incident remediation while collaborating with data scientists and platform teams.

Design, develop, fine-tune, and deploy generative AI models using deep learning and transformer architectures. Collaborate cross-functionally, troubleshoot model issues, optimize performance, document work, and communicate technical concepts to non-technical stakeholders.

Design and operate AWS and Azure infrastructure, modernize operational workflows with AI and automation, build agentic/event-driven self-service platforms, automate identity and endpoint workflows, establish safe AI operational standards, and influence engineering teams with reusable components, observability, and metrics to reduce manual work and improve reliability.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

Entry-level developer working with Python or JavaScript to design, build, test, and maintain software. Collaborate with senior developers, participate in code reviews, debug, and learn new technologies while contributing to the full software development lifecycle.

Design, develop, fine-tune, and deploy generative AI models (GANs, VAEs, transformers). Collaborate with cross-functional teams, troubleshoot model issues, document work, and communicate technical concepts to non-technical stakeholders while staying current on research.

Create AI-generated visuals and interactive prototypes using generative tools and front-end prototyping. Collaborate with designers and engineers to iterate concepts, document workflows, and push generative design and interaction approaches.

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.

The Senior AI/ML Engineer will design, develop, and deploy scalable ML systems focusing on LLM applications, collaborating with various stakeholders to enhance healthcare data insights.

Own and improve Rime's React/TypeScript web app, build conversion and onboarding flows, architect analytics pipelines and instrumentation, implement growth-oriented features, and partner with product, marketing, and sales to measure and optimize user funnels and acquisition.

The AI Support Engineer will troubleshoot complex customer issues, utilize emerging AI technologies for support, and improve operational processes in a hybrid team environment.

Build end-to-end internal applications for Finance and Supply Chain: design React/TypeScript UIs, Python services/APIs, durable workflows, and enterprise integrations (Oracle Fusion, Zip). Own product lifecycle from discovery to rollout and production support, ensuring reliability, security, auditability, and reusable building blocks; apply AI where valuable.

Design, build, and maintain AI platform infrastructure and services. Implement scalable backend systems, REST APIs, CI/CD, observability, automated testing, and cloud-native streaming. Collaborate across teams, apply TDD, and use generative AI tools to accelerate development.

Early-career DevOps role focusing on Kubernetes and AWS operations, embedding security into the development lifecycle, automating processes, and implementing CI/CD solutions. Remote, fixed-term accelerator program for students and recent graduates.

Early-career TechOps role: build dashboards/alerts, script repetitive ticket steps, participate in RCAs, log user pain points and propose fixes, create a lightweight agent (e.g., Telegram bot) with reusable skills, optimize incident-management workflows, and write actionable runbooks to improve support-driven user experience.

Design and build developer-facing AI platform tools, define end-to-end AI development lifecycles and guardrails, deliver reusable tooling and integration patterns, drive cross-functional adoption, mentor engineers, and enable secure, scalable LLM-powered applications.

Lead architecture and adoption of enterprise AI and agentic systems: design multi-agent orchestration, integrate LLMs securely, define CI/CD and reliability standards, lead threat modeling and chaos testing, build reusable platform tooling, mentor senior engineers, and drive cross-team roadmaps and stakeholder alignment.

Design, build, and deploy production-ready agentic AI and multi-agent systems to automate enterprise workflows. Integrate agents with enterprise platforms (APIs, OAuth2, webhooks), prototype solutions, implement guardrails and observability, and deliver scalable automation primarily for marketing operations. Collaborate with clients and cross-functional teams, provide technical leadership, and produce reusable integration and orchestration patterns.

Architect and build production-grade AI applications and shared platform services across desktop, mobile, web, and cloud. Lead full-stack engineering, CI/CD, observability, and operational excellence. Build foundations like agent orchestration, memory/context services, evaluation frameworks, telemetry, and workflow execution. Operationalize NVIDIA AI technologies, collaborate with cross-functional teams, mentor engineers, and drive architectural alignment to deliver scalable, reliable enterprise AI products.

As a Senior AI Developer Technology Engineer at NVIDIA, you'll design and optimize AI and HPC workloads, solve performance issues, and influence future hardware and software design.