Top Remote AI Engineer Jobs

Design, build, and maintain high-performance web servers, APIs, and integration layers. Embed AI capabilities into enterprise workflows, implement secure OAuth2/Okta identity flows, manage CI/CD pipelines, and collaborate with cross-functional teams to translate business needs into scalable technical solutions.

Build and maintain full-stack features across Django/Python backends, React frontends, databases, and shared components. Develop AI-assisted engineering systems using context files, reusable skills, hooks, CI gates, and automated verification. Coordinate concurrent agent workstreams, identify architectural and logic issues, manage dependencies, and improve development processes while balancing technical debt, quality, and feature delivery.

Build and maintain production LLM features, including RAG systems, prompt pipelines, agent workflows, and AI services. Develop Python or TypeScript backend APIs, evaluation frameworks, monitoring, deployment automation, and MLOps tooling. Ensure HIPAA-compliant handling of health data while participating in architecture, code reviews, and engineering standards. The role requires autonomous work in a greenfield environment and collaboration on clinical AI applications.

Design and lead backend platform services, REST and GraphQL APIs, integrations, security patterns, HIPAA-compliant PHI controls, AI/LLM integration, observability, and reliability infrastructure. Contribute to end-to-end full-stack features using TypeScript, Next.js, and React. Establish architecture standards and reviews while supporting scalable services used by healthcare teams and patients.

Develop and optimize machine learning compilers, AI systems, and compiler abstractions for NVIDIA hardware. Build agent-driven automation for machine learning system development, co-design compiler and agent solutions, optimize AI workloads, and collaborate with software and hardware engineering teams. The role focuses on ML compiler technologies, LLM inference and training, GPU acceleration, and productizing machine learning systems.

Drives hands-on adoption of agentic AI development across engineering teams. Embeds with product teams, diagnoses tooling and workflow blockers, configures MCP and IDE integrations, evaluates AI tools, and develops reusable prompts, skills, and automation patterns. The role also shares best practices, informs programme strategy, supports training, and improves engineering productivity across diverse codebases and divisions.

Build and operate Kraken’s internal AI platform, including governed integrations, authentication, identity controls, spend management, provisioning, evaluation tooling, and model lifecycle administration. Develop reusable agent and prompt infrastructure, integrate LLM and SaaS APIs, maintain runbooks, support enterprise users, and lead technical enablement. The role also implements data-handling and retention controls, manages vendors, and improves reliability, automation, documentation, and regulatory readiness across the AI estate.

Build production-ready agentic AI applications that integrate agents, workflows, memory, reasoning, and tools to solve customer use cases. Rapidly prototype, iterate, and ship end-to-end AI experiences; design integrations and UX, ensure reliability, performance, and evaluation; partner with platform and product teams to create reusable components and customer-ready solutions.

Build and ship AI platform features, including agent tool systems, prompt pipelines, integrations, orchestration, and edge-deployed services. Develop reliable LLM-powered applications using TypeScript, Cloudflare Workers, Durable Objects, databases, and AI SDKs. Own features from design through production monitoring, contribute to architecture and technical decisions, mentor engineers, and collaborate with Product, Design, Data Science, and other engineering teams.

Lead end-to-end architecture and production operation of AI systems, including LLM clinical tools, RAG pipelines, agent workflows, and intelligent automation. Evaluate and fine-tune foundation models, build model and agent evaluation frameworks, and ensure safety, accuracy, clinical relevance, HIPAA compliance, and responsible AI governance. Partner with product, design, clinical, and data teams to translate care-delivery workflows into impactful AI features.

Build and scale front-end experiences for Alteryx AI products using React, TypeScript, and micro frontend architecture. Develop reusable UI components, integrate backend services and AI workflows, improve accessibility, performance, testing, and reliability, and guide technical direction. Collaborate across Product, Design, Backend, and Platform teams while mentoring engineers and driving modernization, architecture, and developer experience improvements.

Build and refine AI-enabled prototypes from early business concepts using rapid experimentation and AI development tools. Collaborate with business users and developers, design prompts and agent workflows, integrate APIs, evaluate AI output, and apply data privacy and security practices. Package successful prototypes with documentation and version control for production handoff. The role requires strong judgment, communication, problem-solving, adaptability, and foundational software development skills.

Design, build, and evaluate conversational AI agents using Go and Python. Own agent orchestration, sub-agent architecture, prompt engineering, tool integrations, guardrails, approval flows, observability, and evaluation infrastructure. Develop datasets, scorers, regression suites, and quality metrics for production readiness. Collaborate cross-functionally on conversational experiences, make architecture decisions, rapidly prototype agents, harden them for launch, and mentor engineers.

Architect and build backend systems powering conversational AI experiences. Lead complex projects across the software development lifecycle, define technical direction, design scalable solutions, and ensure system quality. Mentor engineers through code reviews, knowledge sharing, and onboarding while collaborating cross-functionally on product strategy, experiments, and high-value initiatives. Develop LLM-driven infrastructure and maintain operational excellence across production systems.

Lead the architecture and implementation of scalable conversational AI systems and backend services. Own projects from ideation through execution, shape the engineering roadmap, design RESTful APIs and business logic, integrate external partners, evaluate ML-driven results, and optimize platform scalability. The role also includes technical leadership, mentoring, code reviews, cross-functional collaboration, and product-focused decision-making.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Leads delivery of production LLM and AI systems, including multi-provider integrations, RAG, vector databases, agents, MCP tool use, guardrails, evaluation, observability, latency optimization, and cost management. Establishes organizational AI engineering standards, troubleshoots critical features hands-on, manages offshore delivery, reviews designs and code, and reports risks and progress to engineering leadership.

Design, develop, and deploy AI solutions in production; lead AI pilot programs; integrate AI capabilities into existing systems; provide technical guidance; contribute to full-stack development; and optimize AI models for performance and scalability. The role requires expertise across programming, cloud platforms, APIs, databases, SAP, deep learning, NLP, computer vision, and DevOps, supporting financial auditability and transportation logistics for USTRANSCOM.

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.

The role involves architecting a scalable AI platform, building distributed compute solutions, optimizing technologies, and mentoring internal talent in AI and ML.

Develop, train, evaluate, and deploy machine learning models and AI-powered applications. Responsibilities include data preprocessing, feature engineering, deep learning development, cloud-based model deployment, and collaboration with data engineers, MLOps teams, and business stakeholders. The role also requires applying strong analytical and research skills while staying current with advances in artificial intelligence.

Leads architecture and development of a production agentic AI platform. Defines technical strategy, engineering standards, reliability and model evaluation practices, and scalable AI capabilities. Designs RAG integrations, third-party data connections, model routing, tool use, and code execution systems. Partners across Engineering, Product, and Data to drive adoption, resolve technical challenges, and align AI investments with customer outcomes while contributing directly to product delivery.

Design and govern secure, production-grade AI architectures on Microsoft Azure. Responsibilities include Azure AI Foundry workloads, AI governance aligned with NIST AI RMF and ISO/IEC 42001, MCP server security standards, enterprise landing zones, identity and access architecture, RAG and retrieval systems, data classification, security posture management, documentation, design reviews, executive communication, and technical mentorship.

Designs and governs secure AI architecture on Microsoft Azure, including Azure AI Foundry workloads, identity, networking, data retrieval, MCP servers, governance, monitoring, and compliance. Establishes AI governance aligned with NIST AI RMF and ISO/IEC 42001, documents production systems, develops reusable standards, reviews engineering designs, advises executives, and mentors teams.