Top Remote AI Engineer Jobs

Secure AI and cloud-native applications by embedding controls into the Secure SDLC, CI/CD pipelines, Kubernetes environments, APIs, and software supply chains. Lead threat modeling, secure design reviews, vulnerability remediation, container hardening, runtime policy enforcement, and AI/ML workload protection. Partner with engineering teams to establish secure coding standards and improve enterprise security posture.

Own and scale Cobalt’s B2B SaaS go-to-market technology ecosystem, including Salesforce, HubSpot, customer success platforms, integrations, data enrichment, governance, workflow automation, and AI-enabled solutions. Translate business requirements into scalable systems across Marketing, Sales, Customer Success, and Revenue Operations. Manage architecture, data quality, monitoring, permissions, releases, documentation, and technical initiatives from design through optimization.

Build and deploy AI solutions for clients, including agentic workflows, RAG systems, structured extraction, OCR and document-processing pipelines, retrieval systems, evaluation harnesses, and lightweight interfaces. Translate ambiguous client problems into production code, drive engagements from discovery through deployment, collaborate with stakeholders, and contribute to internal platform capabilities and engineering practices.

Leads technical sales engagements for Microsoft Azure cloud and AI infrastructure. Designs secure, scalable, resilient, compliant, and sovereign architectures; guides enterprise customers through workload migration, platform modernization, networking, containerization, and Azure-native adoption. Conducts technical workshops, demonstrations, whiteboarding, prototypes, proofs of concept, and pilots while influencing deployment decisions. Requires expertise across Windows, Linux, Oracle, hybrid cloud, enterprise applications, security, and compliance.

Designs and operates controlled cyber-capability evaluations for frontier AI models and agentic systems. Builds vulnerable applications, cyber ranges, exploit-development targets, and evaluation harnesses; runs experiments; collects telemetry; analyzes model behavior and evaluation artifacts; and documents security findings. The role also develops sandboxing, containment, access, and secrets-management controls while collaborating with red-team operators, researchers, engineers, and Responsible AI stakeholders.

Lead the design and evolution of cloud-native platforms, distributed systems, and infrastructure services at large scale. Define architecture across compute, networking, storage, datacenter, and AI infrastructure environments. Improve automation, lifecycle management, observability, reliability, security, and developer productivity. Guide technical strategy across engineering organizations while contributing to GPU, HPC, bare-metal, networking, and distributed computing platforms. Mentor engineers and foster technical excellence through architecture reviews and cross-organizational collaboration.

Develop and post-train deep learning models while building systems, tooling, and workflows for training, evaluation, debugging, and deployment. Contribute to open-source projects, distributed AI infrastructure, backend services, and developer platforms. Collaborate with research, product, infrastructure, and customer teams to solve complex technical problems, prototype ideas, and productionize successful experiments.

Build and maintain the TypeScript-based Duo Client SDK, including Node.js core packages, public APIs, browser-compatible modules, Vue 3 client components, and platform adapters. Responsibilities include migrating consumers from the GitLab Language Server, managing versioning and deprecation paths, strengthening credential security, optimizing browser performance, writing tests, conducting code reviews, and participating in on-call support.

Build and maintain the Python-based backend engine for GitLab Duo Chat, including agentic LangGraph flows, the Flow Registry, and Duo Workflow Service. Integrate chat capabilities with GitLab’s Rails monolith and GraphQL API, develop secure and tested high-scale features, optimize performance, review code, address technical debt, and collaborate cross-functionally. Participate in weekday, weekend, and occasional nighttime Tier 2 or Tier 3 on-call rotations.

Build reinforcement learning environments to evaluate AI systems on software engineering tasks involving MCP tools. Responsibilities include creating reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature development, codebase refactoring, and performance optimization. The role is remote, flexible, contract-based, and output-focused, with no prior AI experience required.

Lead the architecture and end-to-end development of production-grade AI/ML systems, including training, deployment, monitoring, MLOps, deep learning, NLP, ASR, and agentic AI workflows. Translate research into scalable enterprise solutions, establish engineering and governance standards, evaluate emerging tools, communicate technical tradeoffs to leadership, and mentor engineers. The role requires expertise in Python, deep learning frameworks, cloud ML platforms, ML pipelines, statistics, and production LLM or agentic systems.

Lead design and implementation of AI Ops solutions (RAG, agentic workflows) with enterprise reliability and responsible AI. Improve SRE practices, lead incident response, automate infrastructure with Terraform and GitHub Actions, operate Kubernetes clusters, and develop software using Python and Node.js while mentoring globally distributed teams.

Design and operate scalable batch and streaming data platforms supporting machine learning and generative AI. Build pipelines for structured, unstructured, OCR, document, and image data; develop RAG, semantic search, and LLM-powered solutions; and establish data quality, observability, governance, orchestration, and deployment practices. Partner with stakeholders, lead platform scalability and cost optimization, mentor engineers, and translate ambiguous needs into production-ready technical roadmaps while securely handling sensitive data.

Build and operate multi-cloud platform automation across AWS, GCP, and Azure. Develop Terraform modules, CI/CD components, Python automation, infrastructure provisioning, lifecycle management, drift detection, dependency scanning, and remediation workflows. Integrate AI-assisted and agentic tooling into operational processes while establishing security guardrails. Collaborate with Security, IAM, Network, and FinOps teams on identity, secrets management, least-privilege controls, and cloud governance.

Secure LLMs, generative AI systems, ML infrastructure, training pipelines, and AI applications. Responsibilities include threat modeling, architecture reviews, security controls, hardening, vulnerability remediation, data privacy, incident response runbooks, vendor reviews, governance, policy development, and AI security training. The role also supports proof-of-concept security solutions and monitors emerging AI threats.

Designs and implements enterprise technology solutions across the full IT project lifecycle. Develops solution architectures, evaluates technology alternatives, supports buy-versus-build decisions, aligns physical and logical designs, and collaborates with architects, engineers, and product specialists. Requires experience with multi-tier enterprise environments and utility business systems, including process design, information modeling, data management, infrastructure, security, or application architecture.

Leads enterprise AI tooling and security engineering, designing scalable cloud platforms and secure AI applications. Responsibilities include protecting models, agents, prompts, data, APIs, and outputs; securing the AI/ML lifecycle; implementing identity, encryption, monitoring, threat detection, adversarial testing, and incident response; and establishing engineering standards. The role mentors engineers, influences architecture and technology strategy, and partners with governance, risk, compliance, and business teams to support responsible AI adoption.

Leads enterprise AI tooling and security strategy, building secure cloud-native platforms and protecting AI models, agents, prompts, data, APIs, and outputs. Designs controls for identity, access, encryption, monitoring, threat detection, adversarial testing, governance, and incident response. Provides distinguished technical leadership, mentors engineers, establishes engineering standards, and partners across architecture, security, governance, risk, compliance, and business teams.

Serves as the senior technical authority for enterprise browser data protection and security. Owns architecture, roadmap, standards, lifecycle, browser-based DLP controls, integrations, automation, and operational support. Leads complex troubleshooting, major incident response, root-cause analysis, runbook development, and continuous improvement. Partners across cybersecurity, identity, endpoint, network, cloud, risk, compliance, and vendor teams while mentoring engineers and influencing enterprise security design.

Leads cybersecurity compliance and AI governance across NIST, CMMC, EAR, and ITAR frameworks. Maintains SSPs, POA&Ms, audit evidence, risk assessments, and compliance policies; evaluates AI vendors and use cases; embeds guardrails, access controls, data classification, logging, and review gates into LLM, MLOps, and DevSecOps workflows. Partners with engineering, security, legal, assessors, and government stakeholders to mature organizational compliance and responsible AI practices.

Lead architecture and implementation of a patient-controlled health data platform: custody, consent management, secure EHR integrations, AI-native workflows (LLMs, agents, RAG), mobile-first patient experiences, and build the engineering culture and hiring bar as a founding technical partner.

Lead AI strategy and implementation for go-to-market teams across Sales, Marketing, Customer Success, and Revenue Operations. Build and scale AI copilots, agents, RAG workflows, automations, and integrations that improve productivity, pipeline, customer outcomes, and revenue. Engage customers and sales teams to identify opportunities, manage an AI roadmap, monitor deployed solutions, ensure governance, coach AI builders, and translate AI capabilities into measurable business results.

Design, build, and deploy K-12 AI features (lesson planning, grading, tutoring, admin analytics) while ensuring FERPA/COPPA compliance and child-safety guardrails. Measure impact, iterate with curriculum and legal partners, and maintain production-grade documentation, testing, and runbooks for deployed AI systems.

Design, build, and maintain high-performance web servers, APIs, and integration layers. Embed AI capabilities into enterprise workflows, implement secure OAuth2/Okta identity flows, manage CI/CD pipelines, and collaborate with cross-functional teams to translate business needs into scalable technical solutions.

Build and maintain full-stack features across Django/Python backends, React frontends, databases, and shared components. Develop AI-assisted engineering systems using context files, reusable skills, hooks, CI gates, and automated verification. Coordinate concurrent agent workstreams, identify architectural and logic issues, manage dependencies, and improve development processes while balancing technical debt, quality, and feature delivery.