Top Remote Artificial Intelligence Engineer Jobs in Boston, MA

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Design, build, and maintain secure, scalable, production-grade Generative AI platforms and applications on AWS. Develop agentic AI, multi-agent, RAG, and Amazon Bedrock solutions; create reusable APIs, SDKs, and platform components; implement evaluation, governance, security, observability, and infrastructure automation. Build cloud-native microservices with Python, Terraform, Docker, Kubernetes, and CI/CD. Collaborate with engineering, security, product, and business teams while mentoring engineers and providing technical leadership.

Build and own ClickUp’s AI evaluation systems, agent harness, self-improvement loops, model benchmarking, routing, and fallback strategies. Architect scalable backend and AI platform services for LLM deployment, orchestration, monitoring, logging, and automated evaluation. Improve reliability, privacy, security, and compliance while integrating AI features into the core product and collaborating cross-functionally.

Build and deploy a secure, offline document-intelligence system for RFQ screening. Responsibilities include PDF and drawing processing, OCR, multimodal model deployment, structured citation-grounded extraction, validation rules, audit logging, evaluation frameworks, and local versus Azure OpenAI comparisons. The role also includes packaging signed offline releases, documenting deployments, supporting onsite stakeholder sessions, and assisting client IT teams within strict CUI and air-gapped security boundaries.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Build and operate production LLM systems using Ollama and llama.cpp, develop RAG pipelines and MCP integrations, and create Python backend services for AI features. Deploy and maintain workloads across Azure and AWS using Docker and CI/CD. Automate operations, process structured data, support Kafka-based architectures, troubleshoot across the stack, and progressively develop expertise in model quantization, GPU tuning, and secure offline deployments.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and operate production AI agent systems using Python, LangChain, LangGraph, and Amazon AgentCore. Develop agent tools, Snowflake integrations, retrieval workflows, structured outputs, evaluations, guardrails, and observability. Deploy, monitor, and improve reliable LLM-powered features while collaborating with data engineers. The role requires production Python experience, hands-on LLM development, evaluation framework design, and familiarity with modern AI tooling. Periodic travel may be required.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

Own production agentic AI systems that audit medical claims. Translate ambiguous business problems into measurable technical initiatives, define evaluation metrics, architect agent systems, build reliable Python software, and manage accuracy, latency, cost, and citation grounding. Lead technical direction, review designs, mentor engineers, collaborate with clinical and business stakeholders, and present defensible results. The role requires hands-on experience with agent SDKs, evaluation harnesses, long-context systems, and AI development tools.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, and cost and latency optimization. Develop reliable, measurable, safe, and cost-conscious AI systems; create evaluation frameworks and quality controls; monitor production behavior; and collaborate with backend, platform, product, security, and client teams to deliver practical AI solutions.

Architect, build, and deploy production-grade AI/ML platforms and LLM/RAG solutions. Lead system design, CI/CD and Terraform-based IaC, ensure reliability and monitoring, mentor engineers, run incident response, and prototype emerging AI technologies.

Designs, develops, deploys, and supports large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model evaluation. Defines enterprise AI architecture, optimizes model performance, cost, latency, and throughput, establishes AI safety and governance standards, leads multi-year platform initiatives, and mentors senior technical leaders across engineering and research.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Own application security for Generative AI and emerging products by integrating secure development practices, conducting design and code reviews, threat modeling systems, assessing vendors and AI tools, triaging vulnerabilities, and building security automation. The role partners with engineering and detection teams, contributes to cloud and infrastructure security, participates in incident response and on-call rotations, and documents post-incident remediation.

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.

Implements and supports enterprise AI infrastructure platforms across client environments. Configures Kubernetes, workload schedulers, GPU resource governance, containers, observability, security, and related platform services. Performs discovery, deployment, integration, upgrades, troubleshooting, validation, documentation, and knowledge transfer. Collaborates with cross-functional technical teams, contributes to implementation plans and Statements of Work, manages project deliverables, and provides client-facing consulting support. Travel and occasional extended or weekend work may be required.

Build and ship production AI features for an insurance compliance platform. Responsibilities include developing LLM pipelines, retrieval and agent integrations, document data extraction, evaluation systems, automated quality checks, and AI-powered assistants. The role uses Python/FastAPI, React/TypeScript, and PostgreSQL while emphasizing security, reliability, maintainability, and rapid prototyping. The engineer will own features end to end, investigate complex customer documents and edge cases, and communicate AI tradeoffs to business and product partners.

Leads the architecture, development, deployment, and optimization of large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes enterprise AI architecture, performance and safety standards, and multi-year platform strategies. Partners across engineering, research, product, and program management while mentoring technical leaders and driving responsible AI adoption.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, guide hybrid and multi-cloud strategies, and promote responsible AI practices. Lead technical direction, mentor engineering teams, collaborate with cross-functional stakeholders, and advance research techniques into production.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

Design, build, deploy, and support enterprise AI systems including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for scalability, cost, latency, throughput, and hardware utilization. Establish enterprise AI architecture standards, lead multi-cloud strategies, mentor engineering teams, promote responsible AI, and influence long-term technical roadmaps.

Leads the architecture, roadmap, and delivery of AI/ML and LLM solutions for defense and intelligence missions. Develops projects from proof of concept through production, supports customer demonstrations, collaborates on bids and proposals, and guides multidisciplinary teams. The role requires cloud-native and distributed-systems expertise, stakeholder engagement, innovation, and experience with edge AI, autonomous systems, intelligence analysis, logistics, or readiness programs. Remote work is available with up to 25% travel and occasional on-site requirements.

Build and maintain AI-powered workflows, agents, automations, internal tools, and production systems for creative ideation, copy generation, video pipelines, reporting, and client delivery. Integrate LLMs and third-party APIs, ship rapidly, iterate based on real usage, and develop practical tools that improve marketing operations.