Top Remote Artificial Intelligence Engineer Jobs in Boston, MA

The AI Engineer designs and deploys AI automation tools, partners with teams for workflow automation, and enhances operational efficiency using generative AI and data-driven decision support.

Use advanced analytics, statistical techniques, and machine learning to extract insights from large datasets. Design data solutions and pipelines, build predictive models (R, MATLAB), and apply TensorFlow and Scikit-Learn for deep learning and NLP. Collaborate with teams and clients, deliver quality work, and adhere to firm standards.

The Senior Data Scientist will design and implement AI-enabled products using clinical trial data, mentor team members, and collaborate with stakeholders.

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

Design, build, and ship production-grade agentic AI workflows using LangGraph, Temporal, and Pydantic. Implement AI observability with LangFuse, set AI engineering standards (RAG, prompt management, tool-calling), benchmark LLMs, deploy cloud AI (containerization, cost management), and mentor engineers while supporting enterprise SLA, security, and compliance needs.

As a GTM AI Engineer, design and execute AI-driven workflows for Superhuman's go-to-market stack, improving automation in sales processes and team collaboration.

As an AI Engineer, you will build and scale AI business applications, focusing on generative AI. Responsibilities include rapid prototyping, applying software engineering best practices, and providing production support.

Design and implement AI capabilities, transition prototypes to production, build agent workflows, optimize data retrieval, and automate evaluation processes.

Design, prototype, and deploy LLM-powered agents, RAG workflows, and automations that accelerate internal teams and customer-facing product features. Integrate AI systems with SaaS tools, build reusable components and evaluation frameworks, embed with business teams to deliver measurable outcomes, and establish standards for responsible AI, monitoring, and cost control across the company.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Lead design and implementation of agentic AI workflows and integrations for Acquia DAM. Build, ship, and observe production AI features (auto-tagging, search, governance), set AI engineering standards, evaluate models and tooling, mentor engineers, and represent AI architecture to product and customers.

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

As a Senior AI Engineer, you'll design and implement AI-driven solutions, focusing on full-stack development and production-grade applications while mentoring junior team members.

Design, build, and deploy LLM-powered applications, collaborating with Data Science teams to ensure agent systems operate reliably in production. Handle full lifecycle from prototype to production, integrating APIs and managing workflows.

This role involves defining AI architecture, improving system quality, leading teams, and building production-ready AI systems at Drata.

The role involves designing AI systems for mental healthcare, focusing on building scalable, production-grade systems that enhance patient engagement and clinical operations. Responsibilities include setting technical standards and ensuring safety and reliability while working with advanced AI technologies.

The Applied AI Developer will design and develop AI applications, build infrastructure for agentic AI systems, integrate APIs, and collaborate with stakeholders on AI solutions.

As a Staff AI Engineer, you'll lead AI product development, overseeing technical direction, system deployment, and model evaluation while collaborating across teams.

The AI Business Systems Engineer applies AI solutions across the company, integrating systems, mapping processes, and designing structured workflows to improve efficiency and decision-making.

Lead the creation of innovative AI solutions for healthcare systems, serve as a trusted advisor, foster collaboration, and drive operational excellence while managing complex challenges in healthcare AI.

As a Principal AI Engineer, you'll design production-grade systems integrating generative AI in mental healthcare, overseeing architecture and applied research.

Lead technical design and delivery of production AI/ML and GenAI solutions (RAG, LLMs, agents). Build, tune, deploy, and monitor models and APIs, own MLOps practices, operate cloud AI workloads, ensure software quality and responsible AI, and partner with cross-functional stakeholders while mentoring engineers.

Build agentic applications and tooling with LLM infrastructure, requiring hands-on engineering in a high-pressure environment. Deliver quality under tight deadlines.

Integrate and fine-tune existing AI models into product features, build evaluation frameworks and observability for production AI, collaborate with data science and engineering, and develop AI-powered application features using Python to improve user experience.

Lead operational design and engineering for the data platform: own Snowflake and dbt administration, automate platform operations and CI/CD, monitor platform health and observability, enforce governance and access, optimize performance and costs, and develop AI/agent integrations to enable governed data access for business users.