Top AI Engineer Jobs

Design and implement foundational data systems for AI, focusing on efficiency and performance at scale. Collaborate on systems that optimize data handling and contribute to research advancements.

The Senior Staff Software Engineer will lead the architecture and implementation of AI-powered developer tools and workflows in a cloud environment. Responsibilities include mentoring, driving innovation, managing project delivery, and fostering a culture of continuous improvement.

The Lead AI Product Engineer will design, build, and operate AI agent systems, focusing on architecture, reliability, and performance while collaborating on user-centric workflows.

Design, build, and productionize generative AI, LLM, RAG, and agent-based systems for healthcare. Develop and fine-tune LLM applications, implement vector search and ingestion pipelines, create evaluation/monitoring and safety/governance controls, and collaborate with product and data teams to deliver scalable, reliable AI solutions in cloud-native environments.

Lead design and delivery of scalable Amazon Connect voice and chat solutions. Build Lambda-based integrations, integrate Lex bots and external systems, optimize routing/queues, ensure high availability and monitoring, lead production support, RCA, and continuous improvement for contact center platforms.

Lead design, development, deployment, and maintenance of AI/ML solutions (LLMs, generative models, predictive analytics) and conversational agents. Integrate AI features with web/APIs and cloud infrastructure, ensure responsible AI, security, and operational excellence, mentor team members, and partner with product and architecture to deliver scalable healthcare solutions.

Lead design, build, and operate end-to-end AI/ML-enabled full-stack applications for clinical workflows. Develop Next.js/React frontends, Node.js backends, PostgreSQL data models, integrate LLM capabilities, deploy and monitor on Azure, implement CI/CD and security best practices, and mentor engineers.

Lead design, development, deployment, and maintenance of responsible AI/ML solutions (including LLMs, generative models, chatbots, and agent orchestration) for provider-facing products. Provide technical leadership, mentor engineers, align solutions with enterprise AI strategy and compliance, integrate AI into cloud architectures, and ensure production reliability and operational excellence.

Lead technical direction and people for an AI/ML engineering squad to design, build, and deploy production-grade ML and Generative AI (LLMs, RAG, agentic systems). Drive MLOps, CI/CD, model monitoring, Responsible AI compliance (HIPAA/PHI), architecture reviews, and mentor 5–10 engineers to deliver secure, production solutions.

Design, build, and ship production LLM-powered features for Gusto Retirement. Implement and integrate LLMs (RAG, agents, prompting, tool loops), make pragmatic architecture decisions, collaborate with product, design, and engineering, write reliable well-tested code, and drive end-to-end execution and iteration.

As a Graduate Engineer, you'll design and develop AI agents and features while learning finance operations, coding best practices, and developing APIs in a collaborative environment.

Develop AI agents for finance workflows, design APIs, enhance engineering practices, ensure data security, and facilitate user collaboration.

This role involves developing and implementing testing frameworks for ensuring reliability and performance of AI security controls across platforms. The engineer will expand test coverage and contribute to quality metrics for AI models.

Design, develop, and maintain scalable backend systems for automated order-taking platforms, collaborating with teams to integrate AI models and optimize performance across noisy environments.

As a Software Engineer at Deepgram for Restaurants, you'll design and maintain scalable backend systems for automated ordering, integrating AI models, optimizing performance, and ensuring smooth interactions with restaurant software.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Work on AI-powered Notion features: prototype and ship model-powered product improvements, integrate and productionize models, improve model performance and reliability, and build evaluation frameworks and experiments to measure and improve quality.

The Engineering Manager leads the IAM Platform team, focusing on operational leadership, SRE implementation, automation, AI integration, and team development to ensure stability and innovation in identity management.

Drive company-wide AI adoption by embedding with cross-functional teams to identify use cases and build production AI workflows, agents, internal tools, prompt/skill libraries, evaluation frameworks, and safe deployment patterns. Provide training, office hours, and enablement while partnering with security, IT, legal, and clinical stakeholders to ensure PHI-safe guardrails and measure adoption, productivity, and quality improvements.

Build the canonical customer operating data layer by integrating Salesforce, Jira, Langfuse, Snowflake, product telemetry and other sources. Surface health and adoption signals, build executive reports and visualizations, implement reconciliation, freshness and backfill logic, support automation and AI summaries, and partner with business and BI stakeholders to translate lifecycle definitions into durable, production-ready workflows and data structures.

Design, build, and ship production AI agents and ML systems for retail merchandising, document understanding, retrieval/ranking, and evaluation. Own full model lifecycle from research to production, build evaluation/observability, and collaborate with engineers, PMs, and store owners to drive measurable merchant impact.

Architect, build, and operate an internal AI platform that enables agentic use cases and safe model access. Own platform APIs, SDKs, LLM gateway, provisioning, access governance, license hygiene, observability, and runbooks. Implement AI protocols (MCP, A2A, ARD), enforce identity and policy-as-code controls, strengthen AWS infrastructure (Bedrock, IAM, network security), and partner cross-functionally to drive platform standards and operational excellence.

Build and evaluate LLM-powered agentic systems for security operations: implement features, create evaluation frameworks, automated benchmarks and regression tests, investigate failures, experiment with prompting/retrieval/planning, build infrastructure for deployment and monitoring, and collaborate with customers and product teams to improve agent performance and reliability.

As a Senior AI Engineer, you'll design and deploy AI systems, own AI pipelines, and integrate LLMs into workflows, significantly impacting product direction.

The Forward Deployed AI Engineer will drive AI solutions across departments, build and ship solutions, train teams, and ensure production adoption of AI tools, focusing on measurable impact and collaboration with department leaders.