Top AI Engineer Jobs

Founding AI Engineer responsible for end-to-end R&D and deployment of AI systems (LLMs, diffusion, vision), building rapid prototypes, orchestrating multi-model agentic workflows, integrating diverse APIs, and shipping scalable web applications to support e-commerce and future SaaS products.

Integrate and fine-tune existing AI models into production features, build evaluation frameworks and observability for AI performance, collaborate with data science and engineering, develop AI-powered features in Python, and troubleshoot and monitor models in real-world scenarios.

Build and scale agentic AI applications end-to-end: design, implement, deploy, and iterate full-stack features. Implement agent orchestration, tool calling, RAG workflows, observability, and guardrails. Partner with product, design, data, and business teams; contribute to architecture and mentor engineers. Support production reliability, including possible on-call rotation.

Build and ship full‑stack, production AI-native features that use agentic LLMs to reason, plan, call tools, and act. Implement backend/frontend solutions, agent orchestration, RAG, observability, guardrails, and collaborate across distributed teams to deliver reliable, tested product functionality.

Build and validate computational models of combustion, heat transfer, fluid flows, and fire/explosion scenarios using commercial CFD and FEA tools. Perform pre/post-processing, analyze results, document modeling approaches, run validation experiments, write technical reports, and communicate findings to internal and external stakeholders. Collaborate with industry and academics and present at conferences.

Use CFD tools (FLACS, FDS, others) to model battery fires, gas dispersion, deflagration, and thermal impacts. Perform pre/post-processing, V&V against large-scale tests, support performance-based design and code compliance, produce technical reports, and collaborate with multidisciplinary teams while developing domain expertise.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.

Lead development of a production autonomous-agent intelligence layer: implement closed-loop learning from trade outcomes, build evaluation and automated strategy generation, monitor fleet performance, manage risk and capital allocation, own model training/inference pipelines (including LLM hosting/fine-tuning), and build telemetry and data capture for real-time market adaptation.

Lead design, build, and productionalize generative AI applications and platform capabilities. Automate ML pipelines, optimize models (LoRA/QLoRA/PEFT), implement guardrails and monitoring, support RAG and vector DB tuning, mentor engineers, and collaborate with product and data teams to deploy safe, reliable GenAI features across web and mobile.

This role involves leading AI projects, designing and deploying AI systems, collaborating with stakeholders, and developing tools to enhance efficiency in healthcare applications. The engineer will mentor junior members and iterate on prototypes until widely adopted.

Design, build, and operate production-grade agentic AI systems and LLM-powered features for financial workflows. Implement RAG pipelines, tool invocation, agent orchestration, vector storage, monitoring, and shared AI infrastructure while ensuring reliability, security, auditability, and regulatory compliance.

No responsibilities provided in the job posting.

The AI Engineer will develop and deploy large language models, build infrastructure for high-throughput AI systems, and collaborate with product teams to integrate AI capabilities.

The Senior Software Engineer will design and develop web applications, manage projects, ensure application efficiency, and mentor peers while using modern technologies and AI tools.

As a Senior Applied AI Engineer, you will design, build, and maintain AI systems using LLMs and backend services, ensuring reliability and performance in production. Responsibilities include developing workflows, monitoring AI behaviors, and collaborating across teams to enhance engineering standards.

Design and build an AI platform to resolve insurance claim denials, develop AI agents for workflows, and improve system performance. Collaborate closely with billing managers to understand workflows and contribute to the team's AI/ML vision.

Seeking a Senior-Level AI Engineer to design and maintain LLM orchestration frameworks, integrate AI/ML tooling, and support security assessments.

The Mid-Level AI Engineer will develop AI tools, integrate various systems, manage LLM models, and support security assessments and certification processes.

As an Applied Gen AI Engineer, you'll design and deploy generative AI systems for code review, collaborating to enhance developer productivity through advanced workflows and models.

Design and build AI solutions using Microsoft AI stack (Azure, Copilot Studio, Azure AI Foundry). Develop copilots, RAG and agentic workflows; create API services, implement secure data access, and build CI/CD pipelines. Collaborate with product, engineering, security, and business teams to deliver compliant, observable, and scalable AI capabilities for utilities.

As an AI Engineer, you will enhance backend systems for agentic AI features, design and deploy multi-agent systems, and manage state and context workflows.

The Staff AI Engineer will design and implement production agents for construction plan reviews, focusing on efficient data handling and improved permit processing. Responsibilities include operationalizing workflows, evaluating systems, collaborating on UX, and ensuring reliability and quality in AI products.

The Senior AI Engineer will design and improve AI pipelines, build evaluation frameworks, optimize performance, and collaborate with teams to integrate AI capabilities into Brellium's platform.

Design and build AI-driven systems, analyze large datasets, optimize LLM performance, manage business stakeholder projects, and ensure production-grade analytics solutions.

The GCP GenAI Engineer will design and implement Generative AI solutions on Google Cloud, focusing on deployment, security, and connectivity, while supporting CI/CD pipelines and infrastructure as code.