Top AI Engineer Jobs in Boston, MA

Design and implement AI systems and scalable ML models (Python, TensorFlow); build/maintain data pipelines and integrations; apply NLP (NLTK) and deep learning for analytics; deploy and optimize models (including open-weight LLMs); collaborate with stakeholders and continuously learn to improve AI solutions and deployment reliability.

Lead design and build of an AWS-only AI Operations platform: hosting, IaC, registries (Accountability, Agent, Tool), identity/authorization patterns, developer DX, observability, and security hooks for agentic systems. Evangelize internally and partner on compliance and security integrations.

Build internal AI operations infrastructure and tools (knowledge systems, agent-builder UIs, dashboards, catalogs). Design data flows and decide centralization vs decentralization. Create discoverable, governable surfaces for teams to compose and trust agents. Run a portfolio of small internal products, prioritize requests, and ship production code while partnering with a technical infrastructure counterpart.

Lead the development and deployment of AI-powered products with a focus on foundational AI systems, LLM optimization, and team collaboration to enhance user experiences in banking.

The Lead AI Engineer will develop and deploy AI components, optimize large language models, and improve AI systems performance while collaborating with cross-functional teams.

As a Lead AI Engineer at Capital One, you will design and support AI software, develop LLM and optimization techniques, and collaborate with cross-functional teams to innovate AI products.

As a Senior Lead AI Engineer, you will design, develop, and optimize AI systems while collaborating with a cross-functional team to implement AI-driven products.

As a Lead AI Engineer, you will design and develop AI components, collaborate across teams, improve performance, and contribute to Capital One's AI vision.

The Senior Lead AI Engineer will design and develop AI-driven products, enhance scalable AI systems, and collaborate with cross-functional teams.

Lead engineering teams building AI-native, cloud-based finance platforms. Oversee design, governance, and delivery of distributed microservices and multi-agent AI workflows, mentor engineers, collaborate with product managers, and drive enterprise transformation using cloud, containers, and modern languages/tools.

Lead design and delivery of internal platforms, APIs, data/event pipelines, and AI-powered systems. Drive cross-team architectural direction, operational excellence (SLOs, monitoring, incident response), and mentor senior engineers while establishing standards, AI guardrails, and scalable reference implementations to improve productivity and reliability across the organization.

Build and operate backend and full-stack systems powering AI agent capabilities in Klaviyo Flows. Own end-to-end projects, design scalable async pipelines, REST/GraphQL middleware, and cloud-native infrastructure while partnering with product, ML, and data teams to deliver AI-driven marketing automation features.

The Sr. Lead AI Engineer will design scalable AI services, lead technical projects, improve architectures, mentor engineers, and ensure quality in AI systems.

As a Senior AI Engineer at Klaviyo, you will design and build scalable backend systems and AI solutions, collaborating with product managers and engineers to enhance AI technologies for optimizing customer experiences.

Lead design, build, and deploy production-scale generative AI and agentic systems: foundation model training, LLM inference, vector search, guardrails, evaluation, observability, and performance optimization across cloud infrastructure.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

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.

Lead the development of AI software components for Capital One, including model training, inference, and system optimization. Collaborate with cross-functional teams to deliver groundbreaking AI solutions, ensuring quality and scalability while mentoring junior team members.

As a Lead AI Engineer at Capital One, you will design and develop AI systems, collaborate with cross-functional teams, and optimize AI solutions for performance and scalability.

As a Distinguished AI Engineer, you will design and develop AI software, partner with cross-functional teams, and create optimized AI systems to enhance customer interaction and improve banking services.

Lead design, development, deployment, and optimization of large-scale AI/ML systems (training, inference, orchestration, observability). Collaborate cross-functionally to build responsible, scalable generative AI and agentic platforms, improve performance and cost, and shape long-term ML systems roadmap.

The role involves partnering with cross-functional teams to design, develop, and deploy AI software solutions, focusing on large language models and AI optimization techniques.

Design and build agentic AI features by integrating LLM APIs into product workflows. Write well-tested TypeScript across the stack, implement tool invocation and multi-step reasoning flows, collaborate with Product and Design, and improve reliability and performance of agentic systems while documenting findings.

Lead design, build, deploy, and support scalable, responsible AI systems (foundation model training, LLM inference, similarity search, guardrails, observability). Collaborate with cross-functional teams, optimize model performance and cost, and shape technical vision and roadmap for foundational AI platform services.

Lead design and build of scalable full‑stack AI enablement platforms and tools to accelerate engineering workflows. Champion experimentation, automation (code generation, testing, deployment), best practices, and cross‑team collaboration while mentoring engineers and delivering production‑ready solutions.