Top AI Engineer Jobs

Owns the reference architecture, engineering standards, and technical direction for enterprise AI agents and machine learning systems across cloud and on-premises environments. Designs and implements complex AI platforms, self-hosted LLM solutions, agent architectures, safety guardrails, model governance, and GPU infrastructure. Provides technical mentorship, evaluates emerging tools, partners with data engineering, and serves as the escalation point for complex engineering challenges.

Designs, develops, and deploys scalable AI-powered full-stack applications. Responsibilities include building React or Angular front ends, backend services, APIs, microservices, AI/ML models, LLM and RAG integrations, automation solutions, data pipelines, and cloud-native deployments using AWS, Docker, and Kubernetes. The role also involves database integration, CI/CD, testing, secure coding, rapid prototyping, and collaboration across teams.

Own PlanetScale’s developer experience across AI coding tools, agent frameworks, MCP, CLI, APIs, and documentation. Build integrations, plugins, skills, demos, and agent-focused content; test workflows across leading AI clients; improve tool context and error handling; create agent evaluations and benchmarks; measure usage and recommendation accuracy; and communicate best practices through docs, blogs, videos, social media, and conferences.

Designs, develops, and supports enterprise AI applications, agents, and workflow automations. Builds Generative AI solutions using LLMs, prompt engineering, retrieval-based approaches, APIs, and enterprise data. Responsibilities include full software delivery, stakeholder collaboration, AI output evaluation, secure engineering, governance reviews, monitoring, risk controls, and operational support. The role requires strong software development experience, Python expertise, enterprise integrations, and understanding of responsible AI, security, privacy, and governance.

Lead the architecture, development, deployment, optimization, governance, and observability of scalable AI platforms and systems. Build foundation model training, LLM inference, agentic workflows, similarity search, guardrails, and evaluation capabilities. Establish enterprise AI safety and transparency standards, drive long-term infrastructure strategy, mentor technical leaders, and guide organization-wide adoption of responsible AI solutions.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Define reliability objectives, lead architecture and technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor senior technical staff.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and observability. Lead multi-model orchestration, cost-performance governance, technical design reviews, and AI platform roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while mentoring senior technical teams and promoting responsible AI standards.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, collaborate across engineering and product teams, and mentor senior technical staff.

Design, develop, deploy, optimize, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and orchestration pipelines. Lead cost-performance governance, technical design reviews, AI engineering standards, and long-term platform architecture. Apply state-of-the-art optimization techniques for latency, throughput, scalability, hardware utilization, and inference costs while mentoring senior technical staff and ensuring responsible AI practices.

Design, build, deploy, and support enterprise AI systems including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for scalability, cost, latency, throughput, and hardware utilization. Establish enterprise AI architecture standards, lead multi-cloud strategies, mentor engineering teams, promote responsible AI, and influence long-term technical roadmaps.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish performance governance, lead technical design reviews, shape long-term platform strategy, and mentor senior AI engineering leaders.

Designs, develops, and leads production voice and conversational AI systems using real-time technologies and large language models. Builds automated customer service, payment, and account-servicing workflows; integrates telephony, databases, payment platforms, and enterprise systems; and implements security, compliance, testing, monitoring, scalability, and reliability controls. Serves as a senior technical resource and mentors engineers.

Build Third’s AI-powered creative intelligence platform by designing scalable data pipelines, backend infrastructure, databases, third-party API integrations, LLM workflows, semantic search, and end-to-end product features. The role owns architecture, development, deployment, monitoring, and optimization while collaborating with product and creative teams to turn commerce, creator, engagement, and performance data into actionable insights.

Build production AI systems for finance teams, including per-customer anomaly-detection models, grounded natural-language generation, and agentic workflows with verification checkpoints. Integrate managed LLMs across AWS, Azure, and GCP; develop Python services with SQL and warehouse access; and enforce rigorous evaluation for grounding, hallucination, and faithfulness. The role also involves customer-specific modeling, anti-leakage practices, human confirmation, user-feedback loops, and secure containerized deployments.

Develops AI-enabled industrial engineering and capacity planning tools for Micron’s high-volume semiconductor manufacturing operations. Builds and automates capacity and utilization models using generative AI, Python, SQL, R, Excel, and VBA. Evaluates equipment, process, and roadmap impacts; improves equipment availability, forecast accuracy, and resource utilization; and presents data-driven recommendations through dashboards and executive presentations. Collaborates with Manufacturing, R&D, Operations, Engineering, and Supply Chain teams to support fab production readiness and ramp activities.

Lead development of agentic-AI capabilities and an evaluation/provenance harness to safely automate bulk code changes. Design CI/CD integrations, PR-provenance controls, observability, test/eval tooling, rollout of standards, and operational support to scale machine-authored changes while ensuring auditability, security, and reliability.

Provides technical advisory and systems engineering support for AI-enabled applications in DoD environments. Evaluates LLM, RAG, agentic AI, orchestration, architecture, data pipelines, security, scalability, and model performance. Develops prototypes, recommendations, documentation, and briefings; supports testing, guardrails, and evaluation frameworks; and collaborates with engineers, platform teams, product managers, and government stakeholders to integrate secure AI capabilities.

Build and deploy AI-native healthcare products, including LLM-powered systems and agents for clinicians and patients. Own AI features from experimentation through production, while developing evaluation, monitoring, and feedback systems. Collaborate with clinicians and engineers to identify impactful healthcare applications and optimize reliability, latency, cost, and performance across models, prompts, tools, retrieval systems, and infrastructure.

Build and productionize AI-powered features, including RAG pipelines, tool-augmented agents, and agentic workflows. Integrate AI capabilities into enterprise systems, contribute to evaluation, observability, guardrails, and outcome measurement, and maintain reusable, high-quality code. Work autonomously with stakeholders to ensure solutions fit real workflows, identify technical risks, and support handover and capability-building after delivery.

Build and operate AI platforms, LLM-powered applications, internal APIs, and shared services for Corporate IT Engineering. Develop RAG pipelines, AI proofs of concept, and infrastructure automation while deploying workloads with Docker, Kubernetes, Terraform, Ansible, and AWS. Establish AI development best practices, integrate AI across infrastructure and internal systems, and evaluate solutions for business value. Collaborate with technical and nontechnical stakeholders to improve monitoring, troubleshooting, documentation, identity, and automation workflows.

Lead integration of AI into transportation products: prototype models, evaluate third-party tools, design fine-tuning/benchmarking strategies, oversee computer vision asset extraction, and apply AI to QA/QC with cross-functional teams to improve data integrity and product fit.

Build and ship production-grade AI features, including RAG pipelines, tool-augmented agents, and agentic workflows. Integrate AI capabilities into enterprise systems, contribute to evaluation, observability, guardrails, and outcome measurement, and maintain reusable technical patterns. Collaborate with stakeholders to ensure solutions fit real workflows, identify risks, complete bug fixes, document implementations, and support handover and capability-building.

Designs, develops, deploys, and supports scalable AI applications, generative AI and agentic solutions, automation, and digital agents. The role covers the full AI lifecycle, enterprise AI platform operations, cloud-native engineering, monitoring, security, reliability, and continuous improvement. It leads technical workstreams, collaborates with business and technology teams, and mentors less experienced engineers.

Build and ship production LLM-powered coding tools, internal AI products, multi-agent systems, and agentic workflows. Develop tool-calling, API orchestration, structured output, evaluation, benchmarking, and scalable Python backend systems. Evaluate models based on performance and cost, collaborate across product, security, and engineering teams, and independently deliver AI systems end to end.

Develop AI agents and secure MCP servers for IAM and enterprise applications. Build Python and Node.js backend services, REST APIs, and modern React or Vue.js interfaces. Integrate LLMs and generative AI into existing workflows while applying prompt engineering, tool integration, security, scalability, and performance best practices. Collaborate with engineering and product teams to deliver AI-powered enterprise solutions.