Top AI Engineer Jobs

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.

The Senior Generative AI Developer will design, develop, and implement generative AI solutions, optimize LLMs, ensure robust deployments, and mentor junior team members while adhering to ethical AI standards.

Design, build, and operate a secure, scalable GenAI platform on AWS (including Bedrock). Own platform lifecycle, enable LLM inference, RAG, Agents and guardrails, ensure observability, incident management, security, compliance, cost optimization, and reliability for production-grade regulated-financial environments. Provide Python-based development and support for microservices and REST APIs.

Lead design and productionization of enterprise-grade Generative AI for banking. Own GenAI architecture (RAG, agents, prompt orchestration), ensure scalability, security, observability, and compliance. Partner with business, product, risk, cyber, legal, and platform teams to deliver regulated use cases, mentor engineers, establish reusable patterns, and embed governance, privacy, and guardrails into AI solutions.

Develop and productionize agentic engineering systems for a physical-world AI agent. Build and maintain the agentic harness and tool integrations, run evaluations, analyze production traces, debug failures, collaborate with research to create and deploy custom models, and incorporate new agentic frameworks into the product.

Lead architecture and delivery of LLM-powered data platforms and pipelines: data extraction, transformation, storage, model integration, deployment and evaluation. Drive scalability, reliability, cost-efficiency, engineering standards, and cross-functional collaboration. Mentor engineers, lead architecture reviews, prototype and productionize ML capabilities, and ensure operational maturity (SLOs, observability, on-call). Translate business goals into data products for enterprise customers.

Design, build, deploy, and scale production AI applications and agents. Translate business problems into scalable AI solutions, develop backend services and APIs, integrate with enterprise data platforms, ensure observability, governance, and MLOps, and collaborate with cross-functional client teams.

Design, build, and ship AI-native features and production LLM agents for healthcare workflows. Improve core AI stack (prompt orchestration, retrieval, evaluations), implement safety/guardrails, and deliver full‑stack solutions touching backend infrastructure and member-facing experiences.

Design, build, deploy, and operate production-grade AI systems within delivery teams. Select appropriate ML/DL algorithms, define evaluation methodology, implement CI/CD and IaC, ensure observability and cost controls, and co-hand off operable systems to receiving teams while communicating technical trade-offs to stakeholders.

Design, develop, train, and evaluate state-of-the-art generative AI models (GANs, VAEs, Transformers) and large-scale recommender systems (GNN embeddings). Optimize model performance and scalability, integrate solutions with cross-functional teams, provide technical leadership, and mentor junior engineers to drive AI-driven media buying decisions.

Design, develop, and deploy advanced AI/ML models; optimize pipelines for performance and scalability; integrate solutions with cross-functional teams; research new techniques; mentor junior engineers; monitor and maintain production AI systems.