Top Remote AI Engineer Jobs

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.

Build and ship internal AI enablement tools, workflows, and guardrails to help engineers use coding agents and LLMs safely and productively. Evaluate models and agents, partner with engineering teams, measure adoption and impact, and provide documentation, training, and hands-on support to increase engineering velocity and quality.

Design, build, and operate production-grade agentic AI systems and LLM-powered features for financial workflows. Implement RAG pipelines, tool invocation, agent orchestration, vector storage, monitoring, and shared AI infrastructure while ensuring reliability, security, auditability, and regulatory compliance.

This role involves leading AI projects, designing and deploying AI systems, collaborating with stakeholders, and developing tools to enhance efficiency in healthcare applications. The engineer will mentor junior members and iterate on prototypes until widely adopted.

The Senior AI/ML Engineer will design, develop, and deploy language models for African languages, focusing on NLP applications and cultural context.

As an Applied AI Engineer, you will build and ship AI features, optimize ML models, ensure production reliability, and collaborate with cross-functional teams.

Integrate and scale pre-trained LLMs and semantic search into production SaaS applications. Build RAG/vector data pipelines, multi-step orchestration/agents, backend APIs, automated AI evaluation frameworks, and implement security/privacy guardrails.

Lead technical direction and hands-on development of ML and AI Context systems for HubSpot CRM: model development, productionization, retrieval, embeddings, evaluation, monitoring, and cross-team architectural leadership to deliver measurable product and business impact.

Build and maintain full-stack web and mobile-friendly applications, design and manage Airtable backends, implement workflow automations (n8n/Zapier), integrate LLM/AI services (e.g., Anthropic Claude), optimize performance, collaborate with leadership and vendors, and document systems to reduce single-owner risk.

Lead development and deployment of AI-first systems: build generative and discriminative models, enable multimodal behavior classification from mobile signals, define prompting and benchmarking standards, own ML tooling and SDLC, and partner with Product and Engineering on the AI roadmap.

Work with enterprise customers to design, prototype, and deploy production-ready AI agents using IFS Loops. Integrate with customer systems, tune agent behavior, troubleshoot deployments, and hand off stable solutions while collaborating with Product, Engineering, and Customer Success.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.