Top Remote AI Engineer Jobs

Lead design, development, deployment, and scaling of cloud infrastructure and SRE tooling. Build automation, CI/CD, observability, capacity planning, and reliability improvements; collaborate with product teams to define non-functional requirements and resolve production issues.

Lead AI agent optimization efforts by designing systems that improve token efficiency, response time, reliability, and output quality. Build agentic coding systems, develop AI application patterns, own features end to end, collaborate with engineering and field teams, support the developer community, and create public technical documentation. Work across Python, TypeScript, Java, and Go while serving as a domain expert in agent design and optimization.

Lead implementation of AI strategy and integrate generative AI/LLM capabilities into client applications. Build and operate cloud-native and on-prem model infrastructure, deliver POCs/MVPs with cross-functional teams, optimize LLM cost/token efficiency, and ensure responsible AI, security, and compliance across commercial and public sector environments.

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Build and own production AI-powered products across the frontend, backend, data, and cloud stack. Design multi-agent systems and RAG pipelines, orchestrate AI workflows, and deploy reliable, scalable features. Responsibilities include model serving, latency and cost optimization, prompt engineering, model evaluation, cloud infrastructure, and end-to-end product development using React, Node.js, PostgreSQL, and major cloud platforms.

Develop and operationalize AI-native engineering tools, workflows, standards, and reference architectures across the enterprise. Research emerging technologies, build proofs of concept, integrate approved tools, and create scalable engineering patterns. Develop and deliver AI training, tutorials, documentation, presentations, and demonstrations for technical teams and executives. Support adoption through office hours, change management, stakeholder communication, troubleshooting, and feedback gathering.

Design, build, and deploy production-grade AI/ML and LLM solutions for claims processing, eligibility, prior authorization, and payment integrity. Build scalable data pipelines, integrate models with core payer systems, ensure HIPAA/PHI-safe pipelines and Responsible AI governance, monitor and retrain models, and partner with cross-functional teams to deliver reliable, CI/CD-driven solutions.

Develop AI-native engineering tools, workflows, training, documentation, proofs of concept, and reference architectures across the enterprise. Research and evaluate emerging AI platforms, integrate approved tools into engineering processes, and drive adoption through presentations, tutorials, videos, office hours, and stakeholder communications. The role partners with engineering teams and senior leaders to improve developer velocity, quality, and experience while establishing standards within an IT Software Engineering Center of Excellence.

Lead development of next-generation AI systems for insurance automation, including document validation, voicebots, computer-use agents, and complex workflow orchestration. Set chatbot architecture and technical standards, build and deploy production automations, evaluate emerging AI technologies, and partner with OpenAI on voice models. Own LLM system reliability, including evaluations, regression prevention, latency, cost, and failure handling.

Build and lead next-generation AI systems for automated insurance quoting, customer support, document validation, voice interactions, and external system navigation. Set technical direction for chatbot architecture, evaluate emerging AI technologies, and deploy production LLM systems. Own automation outcomes end to end, including evaluation, regression prevention, latency, cost, and failure handling, while partnering with OpenAI on voice models and workflow automation.

Design, build, and operate production services that enable AI agents to participate in meetings, browse and search the web, communicate by email, make payments, and complete tasks. Develop reliable agent interfaces and workflows, improve platform safety, observability, testing, and operational reliability, and collaborate with product and engineering teams to turn emerging use cases into maintainable products.

Develop AI agents leveraging large language models, integrate external tools, and ensure production-ready AI features while collaborating across teams.

Design and build LLM-powered agents for hardware product development. Responsibilities include ensuring effective coordination among engineering disciplines and integrating AI systems with existing tools.

Serve as the senior client-facing technical lead to design and build data-generation, labeling, and evaluation pipelines for LLMs. Partner with executive stakeholders, diagnose data needs, architect multi-stage automated solutions (RAG, agentic workflows, synthetic data), run experiments, deliver production-ready pipelines, mentor engineers, and own delivery timelines and commercial outcomes.

The AI Engineer will initially function as a Full-Stack Engineer, progressing to integrate AI solutions into products, enhance efficiency, and shape AI application at Sharebite.

Design and implement a self-learning system for autonomous trading agents, automate strategy generation, and build model hosting infrastructure.

Build and ship AI-native systems for a fintech app: AI agents, RAG/retrieval systems, tool-calling and automation for onboarding, KYC, support, risk, CRM and operations. Work with product and engineering to design reliable, low-hallucination, cost- and latency-aware solutions and turn manual processes into scalable AI-enabled systems.

Design, build, and operate internal agentic workflows on OpenRouter to automate support or GTM operations. Integrate with ticketing/CRM systems, ensure reliability via evals, guardrails, and monitoring, measure automation impact, and iterate with support, GTM, and engineering teams.

The Staff AI Engineer will build and scale an AI Agent platform for managing customer interactions, focusing on high-volume, real-time workflows and system performance improvement.

Lead AI-assisted developer experience across TypeScript/Node and Python codebases. Evaluate and govern AI dev tools, optimize developer workflows (builds, linting, tests), own devcontainer strategy, improve test infra, maintain shared libraries and CI/CD, and drive developer onboarding, observability, and platform adoption.

The Senior AI Engineer will develop AI-powered features, mentor engineers, and integrate large language models to enhance user experiences and ensure product reliability.

Develop AI-powered features, collaborate with teams, integrate LLMs, ensure production quality, mentor engineers, and stay updated on AI trends.

Build Apollo’s Constellation control plane for enterprise AI agents. Responsibilities include designing API discovery and schema-understanding systems, developing search, ranking, relevance, and query-planning logic, prototyping retrieval algorithms, and building agent orchestration, tool-calling, evaluation, and observability systems. The role involves rapid iteration in an ambiguous R&D environment, customer collaboration, architectural decision-making, and roadmap shaping.

Own the technical vision and system architecture for Digit’s safety-critical perception systems in dynamic environments. Lead architectural tradeoffs across sensing, ML models, redundancy, latency, compute, and robustness. Define verification and validation standards, support safety cases and hazard analysis, evaluate emerging technologies, and collaborate with navigation, controls, hardware, and safety teams. Mentor senior engineers and represent perception architecture to executives, customers, regulators, and partners.

Leads enterprise adoption of AI-native engineering tools, workflows, and practices. Researches and prototypes emerging platforms, develops proofs of concept and reference architectures, and partners with engineering teams to operationalize solutions. Owns AI training and enablement through documentation, presentations, tutorials, videos, office hours, and executive briefings. Evaluates tooling, optimizes development processes, supports organizational change, and gathers feedback to improve engineering standards and developer experience.