Top Remote AI Engineer Jobs

Design, build, and deploy production-grade AI services for transcript intelligence and deposition workflows. Implement RAG pipelines, LLM integrations, embeddings, vector storage, scalable distributed systems, observability, and attorney-facing features. Maintain and debug Rails, TypeScript, and Python services on cloud infrastructure, and collaborate on architecture, CI/CD, and platform consolidation.

Serve as the senior client-facing technical lead to design and build data-generation, labeling, and evaluation pipelines for LLMs. Partner with executive stakeholders, diagnose data needs, architect multi-stage automated solutions (RAG, agentic workflows, synthetic data), run experiments, deliver production-ready pipelines, mentor engineers, and own delivery timelines and commercial outcomes.

Partner with Sales to design and demo AI-powered, data-driven solutions for life sciences clients using Komodo's Healthcare Map. Run live no-code analyses, build reusable demo assets, navigate AI governance and procurement, quantify ROI, mentor junior SEs, and feed product/AI roadmap input.

Build and maintain cloud- and browser-based AI and automation applications (internal and customer-facing). Integrate with third-party APIs (QuickBooks, Microsoft Graph, CRM, Zoom), implement AI workflows (OpenAI), automate business processes (Zoho, PowerAutomate), and ensure security and maintainability while working closely with leadership and IT.

Drive customer success for AI observability by architecting and delivering proofs-of-value, building integrations (data pipelines and ML infra), advising on model monitoring and governance, and championing customer needs across product, engineering, and sales to drive adoption and expansion.

As a Senior AI Kernel Engineer, you will design and optimize high-performance kernels for AI inference on GPUs and custom accelerators, improving performance and collaborating with various teams to influence architecture and implementation.

The Software Engineer, AI will develop intelligent systems for financial workflows, utilizing LLMs and other technologies to enhance bookkeeping and data processing for agriculture businesses.

The Junior-Midlevel Developer will build and enhance Dumroo.ai's AI-powered education platform, collaborating on software components, data pipelines, and machine learning features.

You will build and maintain AI-native application features, working with a team of AI and backend engineers to ensure quality and innovation.

The role involves architecting scalable backend systems for real-time evaluations, integrating with streaming data, and building intervention layers for AI applications. You'll design frameworks and operationalize vector databases while ensuring low-latency service performance.

The Lead AI Software Engineer designs and implements AI/ML solutions in healthcare, leading initiatives from model development to deployment while ensuring compliance and performance.

Build and maintain secure RESTful and event-driven APIs and MCP server implementations to allow AI agents to interact with enterprise platforms (ServiceNow, Okta, ERP). Implement authentication (OAuth2/OIDC), API gateway policies, audit logging, and monitoring. Collaborate with AI and governance teams, document integrations, and ensure secure, reliable, auditable agentic workflows.

The Data Engineer will develop and optimize data pipelines for LLMs, ensuring high-quality healthcare datasets while collaborating with data scientists on model requirements.

Design, build, and optimize scalable AI-enabled application features and services. Integrate ML models and generative AI, ensure secure and test-driven implementations, troubleshoot AI performance, mentor engineers, and drive AI architecture, CI/CD, and best practices across teams.

Design, fine-tune, evaluate, and deploy LLM-driven, user-facing browser features using RAG, embeddings, prompt engineering, and modern ML tooling. Lead end-to-end model lifecycle, run production experiments, collaborate with product and engineering, and document and review work with an emphasis on privacy, latency, and usability.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Design, build, and deploy enterprise-scale AI systems (agents, retrieval/RAG, generative AI) and reusable platforms. Own end-to-end solutions from discovery through production, testing, observability, and mentoring, driving architecture, standards, and cross-team adoption.

Build and productionize backend-first AI capabilities for a FinOps intelligence platform: design backend services, implement AI features (anomaly detection, recommendations, agents), create evaluation/rollback/monitoring systems, and collaborate with product to ship reliable, cost-efficient, production-ready features.

Lead integration of AI/LLMs into enterprise SaaS and desktop products, evangelize AI-first practices, design and implement scalable .NET/C# backends, modernize legacy architectures, mentor engineers, and collaborate with product and QA across the full development lifecycle to deliver AI-enabled features and agentic workflows.

Hands-on senior role to design, develop, and support AI, analytics, data engineering, IoT, and intelligent automation solutions. Build scalable data pipelines, API integrations, streaming and operational intelligence platforms, generative AI agents, predictive maintenance models, and Power BI reporting to improve operations, asset performance, and decision-making across the enterprise.

Deploy and integrate AI-enabled SOC solutions across customer environments: design cloud-native ingestion pipelines, integrate SIEM/XDR/SOAR and tooling, implement automation and AI workflows, improve analyst experience, troubleshoot production issues, and provide technical leadership and field-facing delivery.

Design, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos) for AI/ML and GPU workloads. Own control plane and etcd lifecycle, build controllers/operators in Go, automate via IaC, improve observability and resource utilization, enable ML training/inference/LLM deployments, participate in on-call incident response, and mentor engineers.

Embed with client engineering teams to operationalize LLMs and retrieval systems across multi-cloud environments. Build agentic solutions using Vertex AI Agent Builder and ADK, connect agents securely to BigQuery/Spanner via MCP, implement streaming pipelines (Dataflow) for real-time inference, and manage model training, tuning, deployment, and evaluation on Vertex AI. Provide consultative guidance, cross-functional collaboration, and clear stakeholder communication to ensure business value from Generative AI solutions.

Lead end-to-end delivery of AI-powered applications: define requirements with clients, translate business needs into technical plans, coordinate cross-functional engineering teams, leverage AI tools for prototyping and delivery, track progress, remove blockers, and drive rapid iteration and continuous improvement.