Top AI Engineer Jobs in Boston, MA

The Lead AI Engineer will design, develop, deploy, and support AI software components, collaborating with cross-functional teams to create scalable AI solutions that enhance customer interactions and operational efficiency.

The Lead AI Engineer will develop and deploy AI software components, optimize AI systems, and collaborate with cross-functional teams at Capital One to enhance customer interactions through AI technology.

Design, develop, deploy, and support foundational AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize training/inference for scalability, cost, latency, and throughput. Partner cross-functionally to deliver production AI services and shape the technical vision and roadmap for foundational AI at scale.

The Senior Lead AI Engineer will design, develop, and support AI systems, leveraging machine learning technologies to enhance customer experiences and optimize AI performance.

Lead design, develop, test, deploy, and support foundational AI systems including foundation model training, LLM inference, similarity search/VectorDBs, guardrails, evaluation, observability, and optimization for scalability, cost, latency, and throughput. Partner cross-functionally and contribute to technical vision and roadmap.

Lead modernization of enterprise AI/ML infrastructure and analyst workflows. Build agentic, scalable, secure platform services, integrate generative AI and low-code/no-code tooling, scale AutoML for tabular and time-series, and engineer automated governance and controls while mentoring teams and influencing cross-functional stakeholders.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.

Lead technical direction and architecture for DAP Repository Flows, design and ship autonomous and scheduled agents that operate on customer repositories, improve DAP onboarding, collaborate across teams on platform interfaces, mentor engineers, and solve high-scale technical problems to raise engineering quality.

Lead development and deployment of foundational AI systems: design, build, test, and support LLM training and inference, similarity search, guardrails, observability, and optimizations for scalability, cost, latency, and throughput while shaping technical vision and collaborating with cross-functional partners.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

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

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.

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.

This role involves leading the development of AI-powered products, optimizing large scale AI systems, and mentoring cross-functional teams. It focuses on designing and deploying AI components, contributing to the technical vision, and applying cutting-edge AI techniques.

Lead design, build, deploy, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production LLM performance (cost, latency, throughput) and contribute to long-term technical vision.

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.

Lead design, development, deployment, and support of large-scale AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, partner with cross-functional teams, and contribute technical vision and roadmap for foundational AI systems.

Lead design, build, deploy, and support GenAI platform services (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, collaborate cross-functionally, contribute to technical vision and roadmap.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

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.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Lead and manage a software engineering team to design, build, test, and deploy AI-enabled applications (LLM integrations, RAG, APIs). Own project planning, budgeting, CI/CD, code quality, observability, microservices, and cross-functional collaboration while coaching and developing team members.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Lead design, build, and operate scalable GenAI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Collaborate cross-functionally, invent LLM optimization techniques, and shape long-term technical vision for foundational AI systems.