Top AI Engineer Jobs

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, and deploy ML and statistical models for threat and anomaly detection; engineer security data pipelines; partner with SOC and engineering to embed analytics into controls and incident response; monitor model performance, and communicate findings to technical and non-technical stakeholders.

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.

Architect and implement scalable generative AI solutions for enterprise document workflows. Own end-to-end delivery, collaborate with product and commercial teams, build extraction and automation features, ensure privacy and security by design, mentor engineers, and drive customer adoption of core platform capabilities.

The Principal Edge AI Firmware Engineer will develop and optimize embedded software for AI applications, focusing on performance and energy efficiency. Responsibilities include enhancing AI runtimes, profiling tools, and ensuring customer integration across embedded environments.

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

The Senior AI Product Engineer will develop and optimize AI product features, ensuring a seamless user experience through collaboration with engineering teams. Responsibilities include building full-stack features, implementing user feedback mechanisms, and monitoring product performance.

Build and lead development of Klaviyo's conversational AI platform: native chat surface, agent orchestrator, and agentic UI. Design scalable, reliable systems, ship high-quality code, mentor engineers, drive technical decisions, and improve AI-powered customer experiences.

Build and operate Coinbase's LLM and agent platform: design the LLM gateway, agent runtime, knowledge bases, AI spend governance, observability/eval tooling, and ship production agents while partnering cross-functionally to ensure secure, cost-effective, and responsible generative AI at scale.

Build and own the production improvement loop for LLM-powered GTM agent workflows: instrument behavior, define evaluation and monitoring, diagnose failures, design and ship behavior improvements, build backend services and feedback pipelines, run experiments and staged rollouts, and partner with product, data, and GTM teams to measure business impact while ensuring safety and reliability.

Design and implement algorithms for packing and allocation in logistics, ensuring operational feasibility. Manage data and integrate solutions into workflows.

As an AI Engineer, you will design and implement systems that enforce feasibility and authority in logistics decision-making, ensuring compliance with command policies. You will work on integrating AI reasoning with execution workflows, handling data validation, and improving the decision-making framework.

Build and operate production agentic AI systems and LLM-powered features for financial services. Implement RAG pipelines, orchestration, and shared AI infrastructure; monitor, debug, and improve reliability. Collaborate with product and design, write production code, and follow security, explainability, and compliance best practices in a regulated environment.

Build and operate production-grade agentic AI systems and LLM-powered customer features. Design RAG pipelines over sensitive financial data, contribute to shared AI infrastructure (LLM services, agent orchestration, evaluation/monitoring), and own reliability, security, and compliance in production. Collaborate with product and design to deliver customer-focused AI solutions.