Top AI Engineer Jobs

Build, optimize, and deploy production-grade generative AI applications. Design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, fine-tune and host LLMs, integrate/swap commercial and open-source models, and deliver scalable FastAPI/async backends with Docker, monitoring, and CI/CD.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

Design, implement, and optimize embodied AI model architectures and deploy them at scale. Build training/evaluation infrastructure and large-scale data pipelines combining simulation, teleoperation, generative and real-world data. Work hands-on with robot hardware (manipulators, mobile robots, RGB-D and tactile/FT sensors) to debug failures and close the sim-to-real loop. Collaborate with global research and hardware teams to integrate systems into consumer products.

Lead design and delivery of AI/ML systems for customer success and support: build production LLM/RAG assistants, model lifecycle and MLOps, low-latency ML services, safety/governance controls, and full-stack integrations (frontend, backend, cloud). Drive observability, KPIs, experiments, and cross-functional partnerships while mentoring engineers and shipping scalable, secure AI features.

Design, develop, and deploy production-scale AI/ML solutions. Architect models and data pipelines, validate and monitor model performance, troubleshoot production issues, collaborate with cross-functional teams, mentor junior engineers, and evaluate emerging AI technologies to drive business impact.

Embed with customers to learn and redesign critical workflows, then build and ship production GenAI/LLM systems on AWS. Own evaluation, deployment, and operational reliability, collaborate with senior stakeholders, and drive measurable business outcomes using Provectus blueprints and agentic tooling.

Design, fine-tune, and deploy large-scale AI models and agentic pipelines (LLMs, RAG, PEFT/LoRA). Build data and CI/CD pipelines, containerized deployments, evaluation frameworks, robustness/adversarial tests, and mentor teams for mission-focused defense AI solutions.

Build AI systems that autonomously turn new model architectures into verified, production-ready kernels optimized for Etched hardware. Develop agentic tooling to generate, compile, profile, and iterate on implementations; design evals for correctness, stability, latency, and efficiency; turn profiler traces and hardware signals into learnable datasets; ship model-generated improvements and partner with architecture teams to influence hardware-software roadmap.

Lead architecture and delivery of agentic AI systems, own the context layer (memory, GraphRAG, knowledge graphs), build robust RAG and retrieval infrastructure, translate client requirements into technical designs, define reusable agentic patterns, and establish observability and evaluation pipelines for production AI.

Design and build autonomous, enterprise-grounded agentic workflows using RAG and ESRE. Integrate and fine-tune LLMs with internal APIs and SaaS tools. Provision scalable cloud infrastructure via Terraform, implement CI/CD, secure network architectures, and deploy observability and tracing to monitor agent behavior. Provide technical leadership, documentation, and enable internal teams to leverage the Elastic Stack for automated business task completion.

Lead design, development, training, validation, and deployment of AI/ML/CV/DL algorithms and models. Architect product features, mentor engineers, run technical reviews, track milestones, and drive agile CI-driven development and process improvements.

Lead architecture, development, training, validation, and testing of AI/ML/DL and computer vision models. Mentor and lead a small team, track milestones and releases, run technical reviews, practice agile and CI methodologies, and drive process and productivity improvements.

Design, build, modernize, and support Microsoft Azure-based applications using .NET Core/.NET 8 and C#. Integrate Azure services (App Services, Functions, API Management, Service Bus, Azure SQL), implement AI solutions with Azure OpenAI/AI Foundry, modernize legacy .NET Framework/Web Forms apps, apply CI/CD and containerization, and collaborate with county staff for knowledge transfer and solution assessments.

Lead architecture, development, and deployment of enterprise-grade LLM and AI systems. Build RAG frameworks, document ingestion and ETL pipelines, orchestration layers, APIs, microservices, and observability for secure, compliant AI solutions in regulated environments.

The AI Engineer will design and deploy machine learning models and AI agents to optimize logistics workflows, collaborating with cross-functional teams to integrate solutions.

Lead the AI development team and own technical execution and solution architecture for enterprise AI: design RAG and agentic workflows, deploy and operate Azure-hosted AI services, build Python/FastAPI back-end services, review code, enforce engineering standards, mentor engineers, and collaborate cross-functionally to ensure secure, scalable AI solutions.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Lead the design and development of AI/ML models for healthcare. Collaborate with business partners and other teams to ensure high-quality execution and adherence to architectural principles.

Build and lead internal AI tooling and automation for security: develop threat-hunting and remediation tools, own large-scale log and ETL pipelines, maintain Kubernetes and serverless infrastructure, partner with product teams to reduce risk, and mentor junior engineers.

Design and operate scalable, event-driven backend systems in Python to power agentic AI workflows and real-time financial infrastructure. Own APIs, data architecture, reliability primitives, and platform foundations while partnering with ML and product teams to productionize AI-native financial systems.

Design and operate scalable, event-driven backend systems in Python for AI-native financial products. Build typed APIs (FastAPI + Pydantic), own data and ledger architectures, develop reliability primitives, partner with ML teams to productionize models, and ship platform foundations for audited, high-throughput financial workflows.

Build and lead internal AI-driven security tooling and automation, support incident response with threat-hunting tools, maintain Kubernetes/serverless and ETL/log pipelines, partner with product teams to reduce risk, and mentor engineers.

As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities.

Optimize production AI inference on Arm-based edge devices by developing kernel-to-system level implementations, profiling and resolving performance bottlenecks, producing production-quality reference code and documentation, and collaborating with customers and internal teams to influence IP and tooling roadmaps.

As a DevOps Engineer, oversee infrastructure for AI systems, manage Kubernetes clusters, GitOps pipelines, deployment strategies, security posture, and platform reliability. Collaborate with feature teams to enhance developer experience and manage infrastructure costs.