Top Remote AI Engineer Jobs

Lead end-to-end IFS Cloud architecture and integrations, design AI-augmented solutions (forecasting, scheduling, anomaly detection), define extension and environment strategies, ensure security/resilience, and align IFS data with enterprise BI/ML initiatives while partnering with stakeholders.

Lead the technical direction of AI agents, managing context and workflows while partnering with teams to align AI initiatives with business goals. Oversee architectural decisions and mentor engineers in AI practices.

Provide hands-on legal engineering support to DACH customers using AI workflows: troubleshoot technical issues, create Help Center content, track adoption/ROI, and enable scalable AI adoption for legal teams.

Provide hands-on legal engineering support for AI-driven legal workflows: troubleshoot self-serve customers, create documentation, track adoption/ROI, escalate technical issues, and enable scalable AI adoption for legal teams.

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.

Design, build, and maintain production-grade AI-powered go-to-market automations (prospecting, lead scoring, campaign orchestration, attribution). Integrate ML/LLM tools into marketing and sales workflows, track business-critical metrics, ensure reliability and scalability, and collaborate with GTM strategists and non-technical stakeholders to drive measurable pipeline and revenue outcomes.

Drive organic growth and visibility by combining SEO, content strategy, and AI. Optimize content for search engines and LLMs (ChatGPT, Gemini, Claude), implement schema/structured data and entity-based SEO, use SEO tools (GSC, GA, Ahrefs, SEMrush) to measure and iterate, and develop strategies to surface client content in AI assistants and Google SGE.

Build and maintain scalable cloud infrastructure and services on GCP to support AI-driven robotics applications. Collaborate with applied ML, web, and R&D teams to design APIs, data pipelines, and ML inference services; participate in testing, performance and cost optimization, and operational reliability. Use infrastructure-as-code, containerization, and orchestration tools while leveraging GenAI tools to accelerate development and improve code quality.

Design and operate large-scale GPU infrastructure for distributed AI training, ensuring reliability, performance, and efficient customer partnerships.

The Site Reliability Engineer will provision and manage Kubernetes clusters, build automation tools, debug customer issues, and improve infrastructure reliability.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

Own product quality for integrations, APIs, dashboards, and AI-agent workflows: build test plans, perform exploratory and end-to-end testing, maintain regression suites, automate high-value tests, investigate production issues, and drive defects to resolution working closely with engineers and product managers.

Lead design and ownership of an Azure-based production AI platform: integrate and standardize LLM tooling (orchestration, observability, prompt/versioning), build secure backend and CI/CD IaC pipelines, harden production operations, enable auditable regulated workflows, and mentor engineers to ensure reliable, traceable AI-assisted certification workflows.

Design, build, and maintain end-to-end agentic intelligence systems that ingest social, on-chain, and ads data; create pipelines, semantic layers, and multi-step AI agents; ship full-stack apps and dashboards; collaborate with marketing and client teams to operationalize structured intelligence and measurable impact.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Lead backend architecture and system design for an AI-driven manufacturing planning platform. Build and scale a Node.js/Python stack with Azure Databricks, implement optimization and scheduling algorithms, establish engineering patterns, and mentor/lead a small early-stage engineering team while collaborating with product and data functions.

Senior DevOps engineer building AI-powered agents, automation pipelines, and full‑stack business applications. Design and maintain integrations (SAP, Salesforce, Google Workspace, Slack, Jira), REST APIs, CI/CD, and containerized deployments. Lead AI standards, prompt engineering, agent frameworks, cross-functional discovery, documentation, and enablement while helping grow an AI Automation & Integrations practice.

Design, test, and maintain reusable prompt templates for enterprise LLM use cases. Run A/B tests, collaborate with trainers and integrators to embed prompts into RAG and AI workflows, and keep prompt libraries updated with best practices and client guidelines.

Develop datasets, models, and evaluation systems for content safety, product security, robustness, and ML fairness for multimodal LLMs. Research and implement bias detection/mitigation, define safety metrics, contribute MLOps automation and safety tooling, and collaborate with engineers, researchers, and data scientists to improve scalable model safety and inclusivity.

Design, build, and productionize Applied AI foundations: model access, retrieval/memory, observability, secure data paths, human-in-the-loop workflows, and reusable AI-driven platform capabilities that support product and operational teams.

Provide onsite and remote technical support for NVIDIA Ethernet and AI infrastructure, troubleshoot Linux-based systems, debug networking and interoperability issues, collaborate with engineering and marketing, document support methodologies, and act as primary customer contact, spending at least one week per month onsite.

Build and launch AI-powered user-facing features on a Ruby on Rails backend. Integrate LLMs (OpenAI, Google, Anthropic), own zero-to-one product initiatives, write well-tested maintainable code, and mentor engineers while collaborating with product, design, and cross-functional teams.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

Lead design, build, and scaling of Agentic AI and industry-specific solution accelerators on Genesys Cloud. Develop reusable multi-agent workflows, RAG-based knowledge orchestration, integrations with enterprise systems, prototypes, and production-grade AI applications. Provide technical leadership, mentor engineers, collaborate with product and industry SMEs, and deliver repeatable solution blueprints to accelerate deployment and time-to-value.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.