Top Remote AI Engineer Jobs

Design and build evaluation and simulation systems for generative AI agents using Dynatrace observability data. Create large-scale simulation pipelines, define metrics/datasets/judging strategies, build developer CLIs, generate adversarial scenarios, and measure agent tool-use and failure modes. Prototype, run feedback cycles, set technical strategy, and mentor engineers.

Lead development of OpenTelemetry ingest for GenAI workloads, validate integrations across AI SDKs and frameworks, align auto-instrumentation with OneAgent, drive OpenTelemetry GenAI semantic conventions, prototype and ship production features, set technical strategy, and mentor engineers.

Lead architecture and delivery of AI-native features for a multi-tenant SaaS platform. Drive cross-functional technical initiatives, design scalable microservice systems, integrate LLMs and agent frameworks, set engineering standards, and mentor engineers to elevate technical execution.

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.

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.

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

Design and supervise technical architectures for intelligent agents and LLMs, implement MCP and A2A multi-agent flows, integrate vector stores and RAG, collaborate with MLOps for continuous deployment, memory and context management, and work with product, UX, data, and backend teams to align solutions with business needs.

Architect and implement enterprise GenAI platforms (RAG and agentic systems), design knowledge retrieval, orchestration, tool-using agents and human-in-the-loop workflows, ensure production readiness and governance with product/data/security teams, define platform engineering best practices, and solve complex platform challenges with broad autonomy.

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.

Design, build, and operate LLM-facing backend services and evaluation systems. Develop prompts, context pipelines, tool-calling workflows, and retrieval systems. Measure and improve model grounding, consistency, latency, and cost. Ship production-ready solutions and rigorous regression/evaluation pipelines for real-world therapeutic data.

Customer-facing engineer who designs, builds, and productionizes GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, collaborate with product and engineering, present at conferences, and optimize/evaluate models and deployments across cloud platforms.

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 maintain highly scalable, reliable observability platform components. Lead architecture, debug distributed systems, reduce technical debt, and elevate engineering quality through testing and code reviews. Mentor junior engineers, collaborate with product teams, and integrate AI tooling to enhance platform capabilities.

Build the core AI platform and high-performance Rust backend powering an AI-native trading copilot. Implement streaming responses, low-latency tool execution, caching, orchestration of models and tools, secure APIs, auditing/tracing, and safe, auditable trading action infrastructure. Partner with product and frontend to enable AI-native UX and translate trading domain requirements into resilient platform primitives.

Lead ownership of reliability, evaluation, and safety for an AI copilot in a trading platform. Build eval systems, benchmarks, automated quality gates, monitoring, and model improvement loops; partner with engineering and product to define safe tool contracts and ensure predictable, auditable assistant behavior in trading workflows.

Build and productionize AI-native workflows, assistants, agents and automation for a neobank app. Integrate LLMs with internal data, APIs, documents and business systems, design evaluation/monitoring/fallbacks, prototype rapidly, then ship reliable, secure systems across customer support, onboarding, payments and operations.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

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.

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

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.

As a Staff NLP Scientist, you will solve complex medical document understanding problems using NLP and Computer Vision, optimizing models for performance and scalability.

The NLP Scientist will solve complex medical document understanding problems using NLP and CV methodologies, optimize models for performance, and make data-driven decisions.

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