Top Remote AI Engineer Jobs

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct threat modeling, adversarial testing, AI red teaming, and security reviews; develop guardrails and automation with engineering teams. Advise executives and the board on AI risk, define organizational controls, and guide responses to emerging threats including prompt injection, model supply-chain risks, and AI-enabled social engineering.

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct AI red teaming, threat modeling, security reviews, and adversarial testing, while building defensive tooling and guardrails. Advise executives and the board on AI risk, guide engineering teams, and define organizational policies for responsible AI security.

Lead DigitalOcean’s AI/ML security strategy, architecture, governance, and multi-year roadmap. Build security controls, guardrails, tooling, and automations; oversee AI red teaming, adversarial testing, threat modeling, and model monitoring. Review AI product architectures, guide high-risk decisions, establish responsible AI policies, and brief executives and the board on AI security risks. Serve as the internal expert and external representative on emerging AI security topics, including agentic AI, model supply chains, prompt injection, and data integrity.

Build foundational systems for AI models across data modalities, develop agentic systems and specialized models, and implement reinforcement learning and active inference solutions. The role involves hands-on architecture and product development, requiring Python backend expertise, cloud deployment knowledge, DevOps practices, and the ability to contribute across the technical stack in an early-stage startup.

Leads software engineering efforts within an agile infrastructure platform team. Develops, reviews, debugs, and governs secure production code using Java full-stack technologies, React, cloud platforms, containers, and relational databases. Drives adoption of AI-assisted engineering tools, establishes validation and security standards, coaches technical teams, promotes SDLC automation, and influences technology decisions. Balances hands-on development with technical leadership and contributes to an inclusive engineering culture.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

Create reinforcement learning environments to evaluate AI systems solving complex software engineering tasks with MCP tools. Responsibilities include designing reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature implementation, codebase refactoring, and performance optimization across software codebases.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

Lead development and evaluation of AI products for medical professionals using large-scale datasets. Create product and client-facing analytics, influence data team strategy, and collaborate with product managers, analysts, and engineers to execute data projects from ingestion through analysis to recommendations.

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Leads enterprise AI/ML architecture for LLM, agentic, multi-agent, and RAG applications. Designs scalable, secure, compliant frameworks; remains hands-on with coding and integrations; drives optimization, observability, reliability, governance, and reusable components. Partners with product, engineering, and compliance teams, evaluates emerging AI technologies, and mentors senior engineers. Requires extensive software engineering and AI architecture experience, strong Python skills, cloud and containerization expertise, and experience deploying AI solutions in regulated environments.

Design, develop, and deploy production agentic AI systems and multi-agent orchestration for conversational healthcare. Build FastAPI and Node.js/TypeScript backends integrating LLMs (Bedrock, Vertex) via GraphQL/AppSync. Implement PII scrubbing, prompt safety, and compliance controls. Deploy containerized workloads to AWS using Terraform and CI/CD. Collaborate with product and clinical stakeholders, monitor production LLM performance and user metrics, and apply responsible AI practices.

Designs, builds, and operationalizes production-grade generative AI and machine learning systems across AWS and GCP. Responsibilities include LLM applications, agentic workflows, RAG architectures, vector search, model lifecycle management, cloud-native microservices, responsible AI governance, HIPAA compliance, and optimization. The role also leads architecture, collaborates with product, security, data, and compliance teams, mentors engineers, and manages team performance.

Designs, develops, and operates enterprise-grade platforms for AI agents and Generative AI applications. Responsibilities include agent orchestration, LLM and AI service integration, secure tool and API invocation, prompt lifecycle management, governance and safety controls, observability, model evaluation, deployment, troubleshooting, and platform optimization. The role collaborates with engineering, security, product, and data teams to deliver scalable, reliable, and compliant AI solutions.

Build AI systems for legal teams by designing legal datasets, annotating training and benchmarking data, defining model requirements, translating legal workflows into structured processes, validating product capabilities, engaging clients, and prototyping with AI tools and scripting. Requires a JD and active or prior bar membership, 2–8+ years of legal practice, strong familiarity with litigation, investigations, compliance, or e-discovery workflows, and comfort collaborating with engineers and product teams.

Develop production-grade multimodal AI systems for L3/L4 autonomous vehicles. Build sensor-fusion and spatiotemporal world models combining camera, LiDAR, radar, navigation, and vehicle-state data. Create multitask models for road understanding, occupancy, perception, prediction, and planning; develop scalable training and evaluation pipelines; investigate foundation-model approaches; optimize models for real-time NVIDIA hardware; and collaborate across AV engineering teams to deploy robust systems.

Build and deploy practical AI solutions, including internal AI agents, intelligent search, workflow automation, document intelligence, custom applications, and SaaS integrations. Establish AI infrastructure, security, governance, evaluation methods, and best practices. Partner with business teams to prototype solutions, identify automation opportunities, evaluate build-versus-buy decisions, and train employees on AI tools.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Design, build, deploy, and optimize production ML and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and chatbot architectures. Own technical delivery from architecture through monitoring and iteration, collaborate directly with enterprise clients, mentor junior ML engineers, and help establish internal technical standards and tooling.

Leads enterprise AI strategy, consulting, solution design, development, automation, integration, governance, and adoption. Partners with business and technical stakeholders to identify opportunities, build agentic AI and generative AI solutions using LLMs and RAG, automate workflows with RPA, and deliver secure, scalable applications. Provides technical leadership for AI governance, platform enablement, process optimization, and responsible AI adoption.

Build and integrate conversational AI systems for outbound voice calls, chatbots, and call auditing. Design dynamic conversation flows, intent and objection handling, escalation logic, knowledge bases, transcription and speech-analysis pipelines, and QA scoring. Integrate transcripts, summaries, and outcomes with GoHighLevel or other CRMs, while supporting telephony portability, restricted data access, and future client deployment. The contractor owns end-to-end execution and submits a proposed scope, timeline, hours, and cost.

Build two AI-assisted reporting systems that consolidate account and internal data, validate KPIs, automate refreshes and recurring report delivery, generate AI summaries, and produce dashboards. The contractor will independently manage requirements, integrations, error handling, documentation, cost assumptions, timeline, and execution under a fixed-scope project engagement.

Own product-layer performance for agentic AI systems in healthcare. Design prompting, retrieval, context, memory, tool use, evaluation, escalation, and human-review workflows. Build production-grounded evaluations, diagnose failures, improve accuracy, reliability, latency, cost, and steerability, and approve model changes. Partner with Product and Engineering while maintaining model cards, intended-use guidance, limitations, and failure-mode documentation for safe, transparent deployment.

Develop and deploy practical AI solutions across internal tooling, AI platforms, and existing products. Build LLM applications using prompt and context engineering, RAG, agentic workflows, and generative AI. Create evaluation frameworks, benchmarks, testing harnesses, and production infrastructure. Apply machine learning algorithms, contribute to secure AWS-based architecture and DevOps, collaborate with product and engineering teams, and mentor junior engineers.