Top Remote AI Engineer Jobs

Lead architecture and development of GM’s in-vehicle AI assistant platform on Android Automotive OS. Build scalable APIs, SDKs, edge-cloud integrations, CI and automated validation infrastructure, and robust AIDL/IPC application contracts. Guide engineering execution, optimize latency and memory, integrate AI/ML evaluation pipelines, and collaborate with product and UX teams. The role requires extensive Android architecture experience, technical leadership, Kotlin and Java expertise, and familiarity with distributed client-server systems.

Develop and implement AI-driven applications, workflows, and integrations on the ServiceNow platform. Gather requirements, configure and customize ServiceNow modules, integrate machine learning and NLP capabilities, develop scripts, manage CMDB and service mapping work, test solutions, troubleshoot incidents, and support deployments across environments. The role also involves stakeholder collaboration, documentation, production remediation, and applying ServiceNow AI features such as Now Assist, Virtual Agent, Predictive Intelligence, and Performance Analytics.

Lead design and deliver SAP BTP AI solutions for Finance. Partner with SAP Finance teams to identify AI use cases, build GenAI capabilities (RAG, Joule Skills, agents), ensure secure, scalable integrations, validate feasibility via POCs, and produce production-ready solutions for reconciliations, controls, reporting, and financial close.

Design, build, and operate production-grade AI tools, automations, and integrations for HR workflows. Lead LLM-based solutions (prompting, RAG), integrate people systems (Workday, Slack, Google Suite), improve data quality, support end users, and drive measurable productivity and product roadmap decisions.

Leads the design, development, deployment, and operation of production AI/ML solutions, including generative AI, RAG, agentic workflows, conversational AI, predictive models, and automation. The role establishes responsible AI practices, integrates enterprise data and APIs, improves evaluation and observability, partners with cross-functional teams, mentors engineers, and drives initiatives from proof of concept through production scale.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, implement, and optimize gameplay AI systems and behaviors for a multiplayer shooter project in Unreal Engine. Collaborate with designers, artists, animators, engineers, QA, production, and leadership to create engaging AI encounters, define architecture, develop test cases, participate in playtests, resolve issues, and manage AI feature tasks. The role requires strong C/C++ programming, substantial Unreal Engine AI experience, and independent technical decision-making.

Lead AI/ML architecture and strategy for sales, engineering, and manufacturing systems. Build production applications for document intelligence, configuration assistance, BOM anomaly detection, labor prediction, quality forecasting, computer vision, and retrieval-grounded assistants. Establish evaluation, monitoring, governance, human review, and safe deployment practices across cloud, on-premises, and edge environments. Improve enterprise data foundations and mentor engineering teams while partnering with product, operations, and program leadership.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

Leads hands-on design, development, deployment, and support of enterprise Agentic AI applications. Responsibilities include architecting multi-agent workflows, RAG pipelines, vector search, LLM integrations, MCP Server tools, and scalable cloud platforms. The role provides technical leadership, code review, mentorship, framework selection, CI/CD and testing standards, responsible AI guardrails, and production support while translating AI strategy into delivery plans.

Develop production AI applications using LLMs, RAG architectures, agentic AI, and multi-agent systems. Build Python APIs and backend services, implement vector search and embeddings, and deploy solutions across Azure AI services. Collaborate with architects, product teams, and stakeholders on enterprise AI projects, architecture decisions, best practices, and technical standards. The role requires strong Python expertise, Azure AI experience, modern AI orchestration frameworks, and CI/CD knowledge.

Build and productionize agentic workflows, RAG pipelines, AI services, backend systems, and ML pipelines for sales automation. Integrate Salesforce, quoting engines, data lakes, and internal platforms using scalable, event-driven architectures. Lead 0-to-1 development of tools for account research, lead prioritization, and quoting while ensuring reliability, observability, performance, and resilience across the GTM technology stack.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Leads Wasabi’s AI infrastructure, data factory, and developer partnerships strategy. Hunts and originates new relationships, develops joint go-to-market and co-selling motions, structures revenue-generating deals, and manages executive partner relationships. The role collaborates with Business Development, Sales, Marketing, and Product, builds the developer ecosystem, tracks pipeline and revenue performance, and represents Wasabi at industry events. Candidates need deep AI infrastructure and storage expertise, an established industry network, and a proven record of closing strategic partnerships.

Build and iterate AI systems spanning computer vision, image manipulation, video generation, and agentic data analysis. Own the full pipeline from model research and API integration through scalable web application deployment. Develop rapid MVPs using AI-assisted tools, orchestrate multiple models into autonomous workflows, and test solutions in e-commerce operations before scaling them into SaaS products.

Build reference software codebases, support NGOs developing generative AI products, integrate diverse data sources, create experimentation tools, and deliver rapid cloud-based solutions. The role includes advising and mentoring partner organizations, conducting rigorous experiments, improving analytics infrastructure, organizing technical workshops, and visiting NGO sites to identify technical needs. Candidates need strong full-stack engineering, backend, data engineering, cloud, database, and generative AI experience.

Build verification and evaluation systems that allow AI agents to determine when work is complete. Develop testing environments, convert failures and human feedback into evaluation signals, improve agent context and memory delivery, and operate the agent fleet as a production system. The role spans application code, infrastructure, testing, measurement, and ambiguous problem-solving. Prior voice, model training, or product-domain experience is not required.

Design, build, and deploy LLM-powered validation pipelines and AI integrations for silicon design and post-silicon validation. Lead cross-team AI projects from prototype to production, evaluate emerging AI tech, and measure impact with data systems to improve workflows and scalability across chip development.

Architect, build, and deploy LLM-powered AI systems and automation to accelerate post-silicon validation and toolchain workflows; integrate AI across engineering teams, evaluate emerging frameworks, measure impact, and operationalize solutions from prototype to production while supporting silicon bring-up and lab debug.

Design and optimize agentic AI systems for NVIDIA’s CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, orchestration, training, verification, reward, and evaluation systems. Co-design AI systems across models, inference, compilers, runtimes, libraries, kernels, and GPUs. The role requires strong C/C++, Rust, and Python skills, GPU programming expertise, and experience with agent systems, reinforcement learning, or modern AI inference.

Design, prototype, and deploy LLM-powered agents, RAG workflows, and automations that accelerate internal teams and customer-facing product features. Integrate AI systems with SaaS tools, build reusable components and evaluation frameworks, embed with business teams to deliver measurable outcomes, and establish standards for responsible AI, monitoring, and cost control across the company.

Build and operate scalable data pipelines, evaluation frameworks, and production AI/ML/NLP models. Own the full lifecycle from data collection and transformation through training, deployment, monitoring, and continuous improvement. Develop robust tooling, observability, and quality checks while collaborating with product and engineering to integrate AI capabilities into user-facing features. Work pragmatically in a fast-moving startup environment and translate business needs into effective data and machine learning solutions.

Lead design, development, deployment, and maintenance of responsible AI/ML solutions (including LLMs, generative models, chatbots, and agent orchestration) for provider-facing products. Provide technical leadership, mentor engineers, align solutions with enterprise AI strategy and compliance, integrate AI into cloud architectures, and ensure production reliability and operational excellence.

Lead the architecture, development, and deployment of scalable AI-powered backend systems. Design APIs and inference workflows, fine-tune and productionize LLM and transformer models, establish MLOps and governance standards, and evaluate AI technologies. Mentor engineers, guide cross-functional technical initiatives, improve system reliability and performance, and shape the organization’s AI strategy and roadmap.

Partners with AI labs to integrate models, run evaluations, deliver results, and manage customer relationships. Builds pragmatic full-stack solutions, automations, onboarding workflows, and delivery tooling for cutting-edge AI teams. Translates customer and research feedback into product and engineering initiatives, supports strategic account growth, and serves as a technical point of contact across onboarding, releases, and issue resolution.