Top Remote AI Engineer Jobs

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

The Staff Forward Deployed Engineer at GitLab works with strategic accounts to adopt GitLab and GitLab Duo Agent Platform, guiding technical discovery and building reusable solutions for complex enterprise environments.

Lead AI products end to end, from prototyping and evaluation through deployment, monitoring, and iteration. Own matching, search, ranking, embeddings, and LLM-powered features while shipping production code across Python services, Rails, and React. Develop evaluation systems for nondeterministic AI, analyze product data with SQL, run experiments, review code, and deliver measurable improvements to healthcare career marketplace products.

Design, build, and deploy production machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and embeddings. Own technical delivery from architecture through monitoring and iteration, collaborate with client engineering and product teams, mentor junior ML engineers, and help establish internal technical standards and best practices.

Develop AI and data solutions for aerospace engineering, including machine learning systems, proof-of-concepts, distributed data architectures, technical code, and production applications. Interns collaborate cross-functionally, present architectures and demos, apply cloud and database technologies, and use methods such as Game Theory and DecisionIQ to deliver business value. The role is remote and offers mentorship, project ownership, and potential return offers.

Individual contributor lead role to architect, build, and deploy agentic LLM systems, RAG/hybrid search, and scalable microservices on cloud/Big Data platforms. Owns MLOps/LLMOps, evaluation frameworks, HIPAA-compliant security controls, and integrations with data and internal AI platforms. Partners with data engineering, mentors engineers, prototypes POCs, and drives production reliability and cost KPIs.

Design and build production-grade GenAI solutions: RAG pipelines, agentic multi-agent workflows, knowledge-base chatbots, vector DBs, LLM integrations, observability, PII redaction, and deploy AI backends with monitoring and documentation.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Leads the architecture, development, deployment, and operation of enterprise AI Ops solutions, including RAG pipelines, agentic workflows, and AI-powered applications. Improves SRE practices, leads incident response, automates cloud infrastructure with Terraform and GitHub Actions, and ensures reliability, scalability, security, and responsible AI. Designs and operates software using Python and Node.js, manages Kubernetes environments, mentors engineers, and leads globally distributed technical teams.

Develop production-grade integrations for NVIDIA Grove across AI frameworks including Dynamo, llm-d, Ray, and PyTorch. Build adapters, plugins, operators, runtime components, reference workflows, and developer tooling. Optimize distributed multi-node and multi-GPU training and inference, improve Kubernetes observability, define APIs, and troubleshoot containers, networking, scheduling, CUDA, and runtime issues. Collaborate with engineering teams and open-source communities to upstream changes and maintain compatibility across evolving frameworks.

Lead production engineering for Cisco’s AI platform by optimizing model routing, latency, token efficiency, batch capacity, and fine-tuning costs. Build reliable validation and regression pipelines for AI agents, ensure generated code is secure and production-ready, and own features from research through deployment, monitoring, and post-mortems. Provide technical leadership, mentor peers, and align cross-functional teams on performance, quality, security, and cost trade-offs.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

Own AssemblyAI’s go-to-market technology stack, including Salesforce, enrichment, outbound automation, routing, scoring, and AI agents. Build production automations, integrations, internal tools, and AI workflows for prospecting, research, personalization, and lead scoring. Convert product usage data into actionable sales signals, automate account targeting, maintain clean funnel data, and partner with Sales and Marketing leadership to improve pipeline and revenue operations.

Architect, build, and operationalize production-grade generative AI and ML systems across Snowflake, Databricks, and Azure ML. Implement RAG, vector search, agentic AI, and API-based model services; create CI/CD, MLOps/LLMOps practices, observability, and governance. Mentor engineers, troubleshoot inference and drift issues, and integrate AI services into regulated healthcare products ensuring security and compliance.

Develop and productionize GenAI applications for federal-sector customers, including RAG, multi-agent systems, Text2SQL, and fine-tuning solutions. Advise customers, lead production rollouts, present technical concepts, collaborate with product and engineering teams, and influence the AI product roadmap. The role requires production machine learning deployment experience, strong data science expertise, customer-facing communication, U.S. citizenship, Secret clearance eligibility, and periodic customer travel.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Design and build agent-based AI features that integrate across BigCommerce and partner systems. Connect agents to MCP servers, enable natural-language store management actions, integrate assistants into the control panel, collaborate with designers and NLP experts, prototype POCs, and contribute to tooling for LLM fine-tuning, prompt engineering, and safe action execution.

Drive technical sales and solution adoption through demonstrations, proofs of concept, hackathons, and architecture workshops. Partner with enterprise platform and engineering teams to design secure, scalable solutions using Microsoft Azure, Dynamics 365, Power Platform, and AI capabilities. Resolve technical blockers, influence solution architecture, support production deployments, build customer relationships, and represent Microsoft through technical thought leadership.

Design, prototype, deploy, and maintain AI-powered applications and services. Build agentic workflows with orchestration frameworks such as LangGraph, apply prompt engineering and RAG, prepare multimodal datasets, evaluate AI models, and select tools for specific use cases. Develop web applications using Next.js, React, and Python frameworks while integrating AI capabilities with existing systems and workflows.

Own quality control, regulatory alignment, and performance reporting for AI-powered clinical extractors. Analyze accuracy, consistency, data quality, provenance, and performance drift; guide extraction logic and clinical interpretation; support FHIR and CSV data exports; and contribute to governance, de-identification, validation, and client reporting. Collaborate with Data Science, Engineering, and Clinical Informatics while serving as a clinical subject-matter expert, particularly in oncology and real-world evidence.

Build and deploy scalable AI-powered web applications and Generative AI workflows. Develop responsive frontend experiences, backend services, AI data pipelines, LLM integrations, and cloud infrastructure. Collaborate with product managers, designers, and AI/ML engineers to translate user needs into product features, optimize application and model performance, and manage cloud deployments. The role requires ownership of production-grade solutions using modern frontend frameworks, backend languages, RAG architectures, vector databases, CI/CD, and AWS.

Lead end-to-end client AI transformation engagements: discover and frame executive problems, design and build production-credible PoAs (data pipelines, integrations, LLM/RAG apps) within 2–3 weeks, present to executives, hand off to delivery, mentor junior FDEs, and convert PoAs into full engagements.

Lead the design and development of mission-relevant software, build backend services and APIs, mentor engineers, and support technical demos.

Own and grow the U.S. corporate sales pipeline for AI-driven software development training. Responsibilities include engaging decision-makers, identifying business needs, tailoring training solutions, selling multi-day programs and ongoing enablement, developing long-term client relationships, and contributing to pricing, packaging, and go-to-market strategy. This is a remote, commission-based, consultative B2B sales role with potential for a future base salary.

Build, maintain, and improve production AI services for document understanding including LLM pipelines, background jobs, databases, and internal tools. Implement features, debug production issues, write tests, and contribute to evaluation frameworks to improve model quality and system reliability. Collaborate with a small engineering team and take increasing ownership over platform components.