Top Remote AI Engineer Jobs

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Design, build, and deploy enterprise-scale generative AI and LLM applications, agentic workflows, and RAG pipelines. Develop full-stack solutions using React/Next.js, Python/Node.js, and PostgreSQL; integrate vector databases and optimize retrieval; lead technical design, mentor engineers, and ensure responsible AI deployment and scalability.

Responsible for bioinformatics ML/AI application development in clinical settings, designing and deploying ML/AI solutions, and collaborating with multidisciplinary teams.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

The AI Engineer will develop agentic AI systems for production environments, collaborating with production teams to optimize reliability and costs, while addressing runtime challenges and building machine learning operations.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

Design, build, and operate a secure, scalable Generative AI platform on AWS (including Bedrock) to support production LLM applications in regulated financial environments. Own platform lifecycle: architecture, deployment, operations, monitoring, incident management, reliability, and performance. Implement LLM inference, RAG, Agents, guardrails, observability, security controls, access management, compliance, and cost optimization. Provide production support, reliability engineering, and AI governance.

As a Senior AI Engineer, you'll design and build AI systems for vulnerability research, developing pipelines for automation and system architecture.

Design, build, and deploy production AI solutions (agents, copilots, RAG) that integrate with Snowflake and dbt, automate workflows, and deliver measurable ROI across the enterprise.

Lead design, development, and productionization of ML and LLM systems for patient- and provider-facing healthcare products. Own end-to-end workflows: problem scoping, data, modeling, evaluation, deployment, monitoring, and clinical guardrails. Build evaluation frameworks, infrastructure, and tooling; partner with clinicians and product; mentor engineers and set technical direction to deliver safe, impactful AI features for clinical care.

Design, build, and productionize ML and LLM systems for patient- and clinician-facing products. Own end-to-end workflows from problem scoping, data and evaluation, training and inference infrastructure, to monitoring and product impact. Define technical strategy, mentor engineers, and collaborate with clinicians and product to ensure safe, effective models.

Design, build, and deploy AI-powered workflows, internal tools, assistants, and platform capabilities to automate operational processes. Integrate LLM APIs, RAG/vector retrieval, and backend systems, collaborate with product and operations, and deliver production-ready solutions that reduce manual work and scale GreenLight's operations.

As a Senior AI Platform Engineer, you will design and build production software for AI Employees, manage technical delivery for client implementations, and ensure system reliability and performance.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

As a Fullstack AI Engineer, you'll prototype frameworks and scale real-time engines, focusing on product feel without handed specs.

Lead API product direction for search and retrieval primitives, partner with strategic accounts to architect integrations, translate customer insights into product specs and roadmaps, run technical deep-dives and documentation, and prototype and ship production code to improve developer experience and platform reliability.

Partner with enterprise AI teams to design, implement, and scale production-grade generative AI observability and evaluation programs. Lead technical discussions, build custom integrations and tooling, manage multiple client engagements from scoping to delivery, and drive best practices across cross-functional teams to influence product and expand customer impact.

Lead delivery of LLM/AI-enabled systems: define LLM selection, prompt engineering, RAG/vector DB and knowledgebase design, AI agents, guardrails, evaluation frameworks, observability, latency and cost management. Manage offshore engineering team, run sprints, unblock delivery, and report status to leadership.

Build production-grade infrastructure for GTM AI agents: harden prototypes, write evals and prompts, implement observability, own deployment pipelines on GCP, manage safe database access, monitor and improve agent fleet, and partner with GTM builders to scale reliability.

Own reliability, monitoring, and incident response for AI infrastructure; build automation and CI/CD tooling; manage Kubernetes/Docker production workloads; partner with infrastructure, security, and compliance; improve observability and documentation; develop internal full‑stack tooling in Go or Python.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.