Top Remote Artificial Intelligence Engineer Jobs in Boston, MA

Develop, train, evaluate, optimize, and debug machine learning models for consumer audio applications. Focus on efficient AI and Edge ML by reducing latency, memory usage, and computational demands. Collaborate with software, embedded systems, and hardware engineers to deploy models on resource-constrained devices. Explore techniques such as quantization, pruning, and model compression while contributing to emerging AI technologies and practical product development.

Lead architecture and delivery of complex AI-native software features for ServiceNow Flow. Build agent runtimes, tool integrations, prompt and context layers, retrieval systems, evaluation frameworks, and human-in-the-loop controls. Establish AI-assisted development practices, mentor engineers, set engineering standards, partner cross-functionally on product delivery, and own production quality, observability, release readiness, and defect resolution.

Lead architecture and delivery of AI-native software for Flow’s conversational service desk. Build agent runtimes, tool integrations, prompt and context layers, retrieval systems, and evaluation harnesses. Establish AI-assisted development practices, mentor engineers, set standards for quality and observability, and partner across product and engineering. Own production quality, defect triage, release readiness, and governance for reliable, secure, enterprise-scale AI capabilities.

Own the implementation, hardening, and polish of game AI behaviors and systems for shipping. Collaborate with design, animation, and other disciplines to tune AI features and support the creative vision. Ensure reliable, stable enemy behaviors and enhance existing architecture. The role requires deep Unreal Engine and modern game AI experience, strong C++ skills, animation systems knowledge, and at least one shipped game title focused primarily on AI.

Leads the design, integration, and delivery of production-grade AI, generative AI, agentic AI, and MLOps solutions for clients. Serves as technical lead, translates requirements into scalable architectures, mentors engineers, manages technical risks and timelines, supports sales proposals, and presents recommendations to stakeholders. Responsibilities include application integration, security, observability, evaluation, model serving, documentation, testing, and operational readiness.

Design, deploy, and operate security controls for Dropbox’s AI and agentic infrastructure across cloud, Kubernetes, data stores, and on-premise systems. Secure authentication, authorization, networking, AI tools, and non-human workloads while addressing prompt injection, poisoning, supply-chain, and MCP risks. Lead security initiatives, harden infrastructure, automate policy and security operations, and collaborate cross-functionally to protect customer data. Participate in on-call rotations as required.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production AI platforms. Define technical standards across HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, reliability, observability, security, and governance. Solve complex performance and scalability challenges, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a highly regulated environment.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Designs and optimizes scalable data pipelines, machine learning models, and AI-enabled enterprise applications. Builds agentic systems, chatbots, autonomous agents, and automation tools; integrates data and AI solutions with business platforms; and ensures data quality, security, compliance, and governance. The role requires Python, SQL, cloud data platforms, modern pipeline tools, MLOps, API integration, and collaborative delivery of reliable end-to-end solutions.

Own the design, development, integration, release, and reliability of tailored AI solutions for enterprise clients. Adapt a core AI platform, deliver client-specific changes to a shared codebase, manage production rollouts, diagnose complex issues, and serve as the primary technical contact for client engineers. The role requires strong Python, software architecture, system design, and experience deploying LLM or agent-based systems used by customers in live environments.

Build and operate production AI agent systems that extract and validate data from documents and spreadsheets, integrate with external systems, manage workflow state and failures, and support human review. Develop evaluation datasets, regression tests, monitoring, and reliability improvements while balancing model use with deterministic code. Collaborate with retirement plan experts, lead technical design, write documentation, review code, and mentor engineers.

The posting provides no specific responsibilities or qualifications. The role title indicates a Full Stack AI Engineer position using .NET.

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

Build and improve the Enterprise AI operating model through AI-enabled processes, operational reporting, knowledge management, stakeholder forums, intake governance, and asset registry oversight. The role translates complex program information into executive narratives, coordinates decision-making, enables cross-functional alignment, and continuously improves planning, prioritization, governance, communication, and execution mechanisms.

Leads architecture, development, and delivery of secure cloud, data, process intelligence, and AI solutions for federal clients. Builds AWS cloud-native services, scalable data platforms, Celonis process-mining solutions, and agentic AI workflows using Amazon Bedrock and Azure OpenAI. Owns technical direction, engineering standards, security, testing, observability, deployment automation, and reusable product packaging. Supports A-123 continuous controls monitoring, agency implementations, demonstrations, and cloud environment deployments while collaborating with AWS and Celonis partners.

Lead the architecture, development, evaluation, and operation of enterprise autonomous AI agents integrated with Salesforce. Build stateful multi-agent workflows using LangGraph, Claude, and MCP; implement evaluation, observability, security, and PII protection; optimize model routing and performance; and establish engineering standards, API contracts, and architecture roadmaps. The role requires extensive software engineering and production LLM experience, strong debugging and stakeholder communication skills, and advanced English proficiency.

Develop and enhance enterprise AI agents, orchestration workflows, conversational applications, and AI-powered automation using Python, LangGraph, and LangChain. Integrate AI solutions with enterprise systems and APIs, implement planning, memory, tool-calling, observability, and monitoring capabilities, and support production-grade generative AI platforms. Collaborate with engineering teams and stakeholders on architecture, reliability, performance, testing, and scalable AI engineering practices.

Develops reusable AI platform components, agent frameworks, connectors, evaluation systems, and developer workflow tools. Builds and tests LLM-enabled applications, implements LLMOps practices, monitors production services, and applies security and governance controls. Partners with software engineering teams to improve AI-assisted delivery through templates, integrations, documentation, onboarding, and technical support.

Build, configure, deploy, monitor, and optimize production AI agents using Microsoft AI Foundry. Develop shared agent tools, customize prompts and tools, implement hosted agents when needed, and enforce security, identity, evaluation, cost, and responsible AI standards. Partner with AI specialists and business experts to test and improve agents, coach non-engineers on prompt evaluation, integrate enterprise systems such as Salesforce and Databricks, and contribute to scalable Agent Factory practices.

Own and productionize AI-powered GTM workflows for Growth, including outbound orchestration, campaign building, enrichment pipelines, intent scoring, paid-media automation, deliverability controls, and seller tools. Build reliable LLM workflows with review gates, observability, testing, and recovery processes. Collaborate with data and platform engineers, measure pipeline impact, manage Snowflake integrations, and continuously improve automation based on replies and meetings booked.

Design, develop, deploy, and support enterprise AI systems, including foundation models, LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Establish AI architecture standards, optimize model performance, cost, latency, and scalability, and lead model routing and orchestration initiatives. Drive responsible AI practices, contribute to technical roadmaps, mentor engineering teams, and influence cross-functional stakeholders and enterprise AI strategy.

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.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.