Top AI Engineer Jobs

Design, build, and scale AI-powered operational platforms integrating LLMs, monitoring, CI/CD, ticketing, and cloud services. Develop APIs, data pipelines, front-end apps (TypeScript/React/Next.js), backend systems, evaluation/observability tools, and reusable frameworks. Collaborate across SRE, Product, and Infrastructure to deliver production-grade, automated AI workflows and improve system reliability and performance.

Design, build, and maintain scalable full-stack systems for underwriting experience, modernizing customer correspondence and automating manual processes using AI. Collaborate cross-functionally, participate in design and code reviews, define product/architecture, and apply DevOps and cloud practices to deliver production-ready solutions.

Develop, test, and maintain AI-powered applications using generative AI and LLMs; collaborate with cross-functional teams to implement AI solutions, analyze data to inform features, and optimize application performance.

Design, test, and maintain reusable prompt templates for enterprise LLM use cases. Run A/B tests, collaborate with trainers and integrators to embed prompts into RAG and AI workflows, and keep prompt libraries updated with best practices and client guidelines.

Design and build full‑stack data capture workflows (Next.js web, Python desktop), architect high‑throughput data pipelines with low latency, collaborate on product strategy, ship features rapidly, and establish engineering best practices for team growth.

Lead and manage a small engineering team while remaining hands-on: build and ship scalable production systems for applied AI, drive execution, recruit and mentor engineers, and collaborate across ops, domain experts, and customers.

The AI Engineer will develop and optimize AI models and high-performance software systems for drug discovery, collaborating with researchers and maintaining infrastructure.

Lead backend architecture and system design for an AI-driven manufacturing planning platform. Build and scale a Node.js/Python stack with Azure Databricks, implement optimization and scheduling algorithms, establish engineering patterns, and mentor/lead a small early-stage engineering team while collaborating with product and data functions.

The AI Software Engineer will develop and deploy AI-powered solutions, build MLOps pipelines, integrate data sources, and ensure model performance in a logistics context.

The Software Engineer AI will design, develop, and deploy AI-powered product features in a cloud-native environment, ensuring technical excellence and collaboration across teams to enhance the candidate experience.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Lead technical architecture and direction for complex AI/ML systems, mentor engineers, and drive innovation in applied AI at GEICO.

The Senior Staff Engineer at GEICO will lead engineering teams to develop AI-powered solutions, ensuring high-quality systems and driving innovation in a fast-paced environment.

Develop, test, and improve AI-powered voice and chat agent workflows: write scripts, configure conversational flows, analyze user data, perform QA, troubleshoot agent behavior, and collaborate with engineers and designers to optimize performance.

Develops and maintains application software and system components; monitors and administers applications; provides technical and application support; ensures data integrity and security; may work with vendor software and manage small projects.

Design, integrate, and deploy AI/ML systems for defense products: develop inference services, data pipelines, model evaluation and robustness testing, prototype to production, and optimize edge/GPU deployments while collaborating with product and mission teams.

Build and ship end-to-end product features, debug and resolve production issues, partner with Product on UX and direction, use AI heavily for development and prototyping, and iterate quickly across the stack to improve quality and speed.

Lead technical delivery of AI-enabled web apps, dashboards, and automation. Build full-stack systems (frontend, backend, infra), integrate LLMs, RAG, vector DBs and third-party services, prototype then productionize solutions, work client-facing for scoping and translation, and provide technical leadership across architecture, standards, and documentation.

Design and build the intelligence layer: retrieval and memory systems, RAG pipelines, feedback and evaluation loops, RL and fine-tuning experiments, LLM integration, observability for AI performance, and collaborate to productize AI features.

Lead production agentic development and build R&D tooling that accelerates team workflows. Ship weekly in the main codebase, create and improve custom agents, MCP integrations, evals, and codegen pipelines, train engineers on new workflows, and raise team standards for prompts, tooling, and autonomous throughput.

Own and build Tern's AI and data systems end-to-end: data pipelines, production AI services, evaluation harnesses, monitoring and reporting. Ensure data quality across ingestion, modeling, and reporting, ship reliable production software, and collaborate with product squads to deliver trustworthy AI features and advisor-facing analytics.

As an AI Prompt Engineer, you'll develop prompt templates, conduct A/B tests, collaborate with trainers, and maintain prompt libraries for generative AI applications.

Lead the design and evaluation of conversational AI systems from data preparation to model deployment, focusing on transforming unstructured conversations into actionable insights.

Lead design and evaluation of applied AI systems for conversational intelligence. Responsibilities include model architecture, evaluation frameworks, and collaboration with cross-functional teams.

Lead architecture and evolution of AI-powered systems, ensuring they are reliable and scalable. Oversee delivery of AI-native systems and establish standards for API contracts and evaluation frameworks.