Top Remote AI Engineer Jobs

Design and ship agentic LLM pipelines and full-stack AI features using Python, FastAPI, TypeScript, and React. Evaluate prompts and models for accuracy, latency, and cost; build evaluation frameworks, regression testing, observability, and feedback loops. Partner with product teams on AI use cases, contribute to architecture, and guide responsible AI adoption in a fast-paced startup environment.

Architects and builds next-generation conversational AI products using generative AI. Leads and mentors engineering teams, establishes technical direction, partners with Product on scalable solutions, designs resilient cloud-native architectures, promotes AI tools and services, and drives quality through test automation. The role requires extensive full-stack development, enterprise SaaS architecture, production AI delivery, cloud platforms, and technical engineering leadership experience.

Design and build distributed systems, high-performance APIs, real-time data pipelines, and intelligent LLM-powered applications processing billions of events daily. Develop advertising, identity, and intent platform capabilities using streaming, batch, and large-scale data technologies. Own system quality, scalability, performance, and observability while collaborating with product managers, data scientists, and engineers. Provide technical leadership and mentorship across initiatives involving ad infrastructure, identity resolution, behavioral analytics, and applied AI.

Designs and deploys AI assistants, copilots, custom agents, and end-to-end workflow automations across business functions. Builds integrations between AI platforms and enterprise systems using APIs, middleware, and serverless architectures. Develops prompt, RAG, and agentic solutions while measuring ROI and adoption. Partners with IT Operations and Security on governance, privacy, compliance, and secure deployment. Documents solutions, trains teams, and continuously improves automations based on usage and feedback.

Lead the architecture and deployment of scalable data platforms, real-time and batch pipelines, AI infrastructure data systems, APIs, data lakes, warehouses, streaming architectures, and data quality frameworks. Optimize distributed processing and queries, establish governance, model enterprise data semantics, evaluate modern data technologies, and mentor engineering teams while collaborating across analytics, product, and platform groups.

Design, build, and deploy agentic AI systems, orchestration pipelines, and RAG/knowledge retrieval services. Implement scalable backend services, observability, LLMOps practices, and integrations with code repositories and enterprise systems. Mentor engineers and ensure explainable, traceable AI for regulated federal healthcare environments.

Design and build identity services for AI agents and enterprise systems: authentication/authorization (OAuth2/OIDC/JWT/SAML/mTLS), credential lifecycle, enterprise IdP integrations, secure agent access, developer-facing identity SDKs/APIs, auditability, and compliance alignment for FedRAMP/Zero Trust environments.

The role involves engaging with AI-native companies to optimize their AI platforms using NVIDIA technologies, focusing on architecture, model performance, and collaboration with technical teams.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

The AI Engineer will design and deploy classification models for media, optimize data pipelines, lead R&D in voice and audio generation, and research image intelligence technologies.

As a Senior AI Engineer, you will build and scale the AI Agent platform, handling customer interactions across multiple channels, while collaborating with cross-functional teams to deliver effective solutions.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

As an AI Engineer, you'll develop and optimize LLMs, collaborating closely with cross-functional teams to enhance detection engineering capabilities and drive new AI initiatives.

As an AI Engineer, you will develop and optimize AI systems, focusing on LLM integration, vector databases, and RAG pipelines, collaborating with various teams to ensure cutting-edge performance.

The Senior AI Engineer will develop AI-driven solutions using LLMs, focusing on prompt engineering, integration, research, and data utilization for payment systems.

The Principal AI Engineer will build and improve the core AI tutoring system, design evaluation methods, and collaborate on pedagogical goals, driving innovation in learner experiences.

Build and evolve an AI tutoring system, run evaluations, design software, collaborate with teams, and ensure quality through testing and refinement.

Responsible for building and evolving an AI tutoring system, running experiments, collaborating with teams, and ensuring quality in model behaviors.

As a Principal AI Engineer, you'll build and evolve AI tutoring systems, run experiments for learning effectiveness, design software, collaborate across teams, and prototype AI interactions.

Design, build, and operate customer-facing AI systems across the full product lifecycle: discovery, prototyping, deployment, evaluation, and monitoring. Translate ambiguous customer problems into AI solutions, own system design and scalability, ship production code, and partner with product teams to apply frontier models and agent frameworks.

Lead technical design and delivery of enterprise AI solutions (generative AI, RAG, agentic AI, predictive models). Own architecture, deployment, responsible AI standards, vendor evaluations, prototyping, CI/CD/LLMOps, monitoring, and mentor engineers while aligning solutions to business outcomes and regulatory best practices.

Design, build, deploy, and maintain production AI systems (LLM-powered and agentic) to automate healthcare reimbursement workflows. Own end-to-end model development, evaluation, monitoring, and continuous improvement; collaborate with product, data, and operations to translate requirements into reliable, scalable systems while ensuring safety, observability, and compliance with healthcare data standards.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Lead deployment and scaling of Phaidra's AI-driven control solutions for data centers and industrial facilities. Model facilities, configure and commission products, triage and support deployed systems, translate customer needs into product requirements, and act as a technical SME for customers and internal teams. Coordinate cross-functional delivery, mentor peers, and contribute to product and sales initiatives. Role may include up to 25% travel.

Design, build, and operate core systems for autonomous agents, focusing on reliability, security, and engineering effective agentic workflows and integrations for enterprise environments.