Top AI Engineer Jobs

Lead design, development, deployment, and support of large-scale AI systems (LLM training/inference, similarity search, guardrails, evaluation, observability). Partner cross-functionally, optimize LLM performance (cost, latency, throughput), and shape technical vision and roadmap for foundational AI platforms.

Lead design, development, deployment, and support of foundational AI systems including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI performance (latency, throughput, cost), collaborate cross-functionally, and contribute to technical vision and roadmap for production AI infrastructure.

Design, implement, and operate production-grade agentic AI and LLM-enabled services on AWS. Build retrieval-augmented systems, APIs, and integrations; optimize performance, scalability, cost, and observability; implement evaluation, testing, guardrails, and secure development practices while collaborating across product and engineering teams.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.

Lead and mentor a team to design, build, and operate agentic LLM systems and production LLM-backed APIs. Own end-to-end ML lifecycle, MLOps, inference optimization, vector memory, safety/guardrails, and cross-functional governance for scalable, secure AI deployments.

Lead and guide agile teams to design, build, and deliver secure, scalable AI-enabled software. Develop and review production code, drive product and technical decisions, champion enterprise AI-assisted development practices, implement RAG/LLM architectures and vector stores, integrate cloud services and CI/CD, and establish responsible AI guardrails and validation standards.

Lead an agile engineering team to design, build, and deliver secure, scalable AI-enabled products. Provide technical guidance, write and review production code, drive product and architecture decisions, adopt enterprise AI-assisted development practices, implement RAG and LLM solutions, and ensure responsible AI guardrails and evaluation frameworks.

Design and implement AI/ML solutions, analyze complex problems, mentor junior team members, build client relationships, integrate AI into applications using Oracle data/analytics tools, and collaborate cross-functionally while upholding professional and ethical standards.

Lead design and implementation of agentic AI workflows and integrations for Acquia DAM. Build, ship, and observe production AI features (auto-tagging, search, governance), set AI engineering standards, evaluate models and tooling, mentor engineers, and represent AI architecture to product and customers.

Design, build, and deploy production-grade AI and Generative AI solutions. Collaborate with data scientists and platform engineers to implement scalable, observable models, manage ML experiments, automate deployments, and review code. Prototype new AI capabilities and apply LLM/GenAI patterns (RAG, agentic workflows) for enterprise integration.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

Lead end-to-end delivery of AI solutions (ML, Generative AI, agentic systems), design and deploy production-grade LLM/RAG and multi-agent architectures, define responsible-AI standards and evaluation, mentor engineers, and partner with stakeholders to measure and drive business value using cloud AI platforms and enterprise CI/CD/IaC.

Lead end-to-end delivery of production AI solutions (ML, GenAI, agentic systems, RAG) on the enterprise AI platform. Design, prototype, deploy, monitor, and evaluate solutions; set technical standards; mentor engineers; and partner with stakeholders to drive measurable business value and adoption.

Lead technical development and integration of physics-informed AI, surrogate models, and digital twins for aerospace CFD, structural, thermal, and system-level simulation. Drive AI strategy, lead R&D projects, secure funding, mentor engineers, and transition models into engineering workflows.

Lead AI engineering efforts to develop physics-informed ML, surrogate models, and digital twins for aerospace systems. Define AI strategy, integrate surrogates with CFD/FEA workflows, pursue R&D funding, mentor engineers, and transition models into engineering design processes.

Own verification architecture and eval harnesses that produce adversarially-verified, tiered product claims. Build oracle-separated verifier agents, regression corpora, and routing laws to ensure throughput and truth-quality gates. Author corpora specs, measure judge bias, enforce deterministic verification gates, and prove token ROI across agent loops. Work directly with founder and platform teams to ship verifiers, corpora, and model-routing rules that scale verified claims safely.

Design, own, and operate a production agent harness and long-running AI automations. Build verification systems (oracle-separated checkers, regression corpora), deterministic liveness checks, instrumentation, CI gates, and model-routing/token-economics to ensure correctness and measurable outcomes. Work directly with founder across product, ops, and verification; ship alarms, regression suites, and escalation paths that scale human review only where judgment is required.

Architect and scale an internal AI platform and APIs to integrate LLMs into developer workflows. Build autonomous agents, RAG systems, and production-grade tooling to boost developer productivity, ensure reliability, security, and cost-efficiency, and collaborate across product, infrastructure, and security teams.

Design and build an enterprise generative AI platform: agent orchestration, memory, guardrails, vector search, SDKs, and blueprints. Standardize agentic workflows, optimize orchestration and cost, own Helm/Kubernetes operators, ensure trust and safety, mentor engineering teams, and evangelize platform architecture internally and externally.

Lead design and delivery of an agentic AI platform: define architecture, standardize agentic workflows, build GenAI SDK/CLI, harden guardrails and observability, optimize orchestration and costs, own Helm/K8s operators for autoscaling, incubate RFCs and proofs of concept, and mentor engineering leaders while evangelizing platform practices internally and externally.

The Senior Lead AI Engineer at Capital One will design, deploy, and support AI systems, working with cross-functional teams to innovate and enhance customer interactions while ensuring scalability and performance.

Lead design, development, deployment, and support of production-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability). Drive LLM optimizations, technical vision, and cross-functional delivery of responsible AI products.

Lead a cross-functional team to deliver AI-powered banking products, focusing on developing and supporting AI software components and optimizing LLM technologies.

Lead design, build, deploy, and support foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and optimization. Partner with cross-functional teams to deliver scalable, performant, and responsible generative AI platform services and contribute to long-term technical vision and roadmap.

The AI Engineer will develop production-grade AI tools, bridge business needs with technical solutions, and mentor other engineers while ensuring scalability and security in AI integration.