Top Remote Artificial Intelligence Engineer Jobs in Boston, MA

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Build and operate Coinbase's LLM and agent platform: unified model gateway, AI Hub, secure microVM agent runtime, knowledge base integrations, AI FinOps, observability and evaluation tooling, and production-grade agent deployments with teams across the company.

Design, build, and operate a secure, scalable enterprise Generative AI platform on AWS (including AWS Bedrock). Own platform lifecycle: architecture, deployment, operations, monitoring, incident management, reliability, and governance. Implement LLM inference, RAG, Agents, guardrails, observability, security controls, access management, compliance, and cost optimization. Support production-grade GenAI applications in regulated/financial environments.

Lead AI Engineer defines AI strategy, architecture, and standards; makes build vs. buy decisions; architects scalable, reliable LLM/GenAI systems; establishes LLMOps, evaluation, guardrails, and observability; coordinates cross-functional alignment with product and engineering; and champions AI-assisted developer tooling and responsible AI practices.

Customer-facing applied AI role advising enterprise clients on end-to-end AI deployment: model selection, distributed training, inference optimization, data pipelines, evaluation/alignment, and proof-of-value prototypes. Lead technical discovery, develop enablement content, publish and present externally, and collaborate across sales, engineering, and partners to translate AI research into production outcomes.

Build intuitive, high-performance React interfaces for AI-powered insurance workflows. Collaborate with design and backend teams, create reusable UI components and design systems, integrate frontend features with AI services (document review, entity extraction, quote comparison), rapidly prototype, and iterate based on user feedback.

Work directly with insurance customers to deploy and configure AI-powered workflows, integrate with agency management systems and messy customer data, debug integrations, build tooling, and relay product requirements to the core platform while partnering with sales and customer success.

Build and productionize AI features across frontend and backend: document review, entity extraction, quote comparison, embeddings and LLM integrations, prototype from customer feedback, and create scalable reusable systems for insurance workflows.

Design, develop, and support cloud-native finance and actuarial applications and integrations. Build AI-powered solutions (LLMs, RAG, conversational agents, document intelligence), automate financial workflows, partner with data teams for pipelines and dashboards, and ensure security, compliance, and operational performance.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

Build end-to-end AI systems from raw context to production agents: design agent surfaces, backends, APIs, integrations, and observability/evaluation frameworks; participate in client scoping and SOWs; drive tooling, review AI-generated code, and create reusable playbooks.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business data (BigQuery, CRMs, Slack), build MCP servers, APIs, and scalable serverless/containerized services, implement observability and governance, and partner with GTM teams to deliver measurable automation and self-service workflows.

The AI Engineer will design and build production systems, focusing on LLM integration, system reliability, evaluation frameworks, and high-quality product features while maintaining strong engineering practices.

As a Senior Agentic AI Engineer, you'll develop core systems for AI agents, focusing on reliability, observability, and production readiness, working hands-on with distributed systems and orchestration.

As a Full Stack AI Engineer, you'll build AI applications and products, prototype solutions, and collaborate with founders in a fast-paced environment.

As a Forward Deployed AI Engineer, you will architect, build, and deploy Generative AI systems while working closely with customers to ensure mission success. Your role involves mission-driven software delivery, collaborating with various stakeholders, and defining solution roadmaps.

Design, build, and deploy production-grade ML and LLM systems (RAG, fine-tuning, embeddings, agentic workflows). Own end-to-end technical delivery, collaborate with client teams, mentor junior engineers, improve MLOps/CI/CD, and shape internal best practices and tooling for scalable AI products.

The Forward Deployed AI Engineer will deploy AI solutions, work with clients on integration, and build prototypes while contributing to platform development.

Lead enterprise model-training strategy for Commercial AI products: fine-tune LLMs (QLoRA/LoRA/PEFT), train traditional ML models, design large-scale data pipelines, enforce data governance and PII handling, build reproducible experiment/training pipelines, optimize GPU/distributed training costs, and mentor teams to operationalize models into production.

Lead design and implementation of the enterprise AI orchestration and retrieval platform: build MCP servers, define tool-surface and multi-agent patterns, design RAG systems and chunking, implement routing/context/window management and observability, set evaluation and safety standards, onboard product teams, and mentor engineers to raise orchestration and retrieval maturity.

The Software Architect will lead AI quality evaluation, design evaluation frameworks, create risk controls, and ensure compliance, partnering with various teams on AI systems.

This role leads data and AI engineering strategies for R&D, oversees a team of engineers, and ensures compliance with regulatory standards while fostering innovation and collaboration across functions.

The Senior AI Engineer will develop and deploy multi-agent systems, automate workflows, integrate enterprise systems, and mentor junior engineers while ensuring quality and security compliance.

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.

The Staff AI Engineer will build the AI platform, apply LLMs for intelligent feature development, and ensure system scalability and security while optimizing performance.