Top AI Jobs in Boston, MA

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 and execute AI integration across finance and legal, owning strategy, sequencing, and delivery. Build and lead embedded AI Fellows, productionalize LLM-driven tools, drive adoption, measure business impact, and coordinate with external partners to consolidate initiatives and ensure enterprise readiness.

Lead enterprise AI integration across Product Development and Product Excellence. Build unified data infrastructure, deploy AI tools, reform program management, scale AI-powered quality and consumer insights, and lead an embedded AI Fellows team to drive adoption, measurable outcomes, and organizational change across hardware and consumer product lifecycles.

Lead AI and automation product strategy for Finance transformation: define and launch AI features and automated workflows, manage model lifecycle with cross-functional teams, ensure responsible AI and compliance, and use AI metrics to iterate and deliver measurable operational leverage.

Lead the engineering team to develop Tulip's new agent-native app-building product, managing technical architecture and fostering AI-driven development practices.

Lead and deliver large-scale AI/ML programs and the next-generation Generative AI platform. Build and grow a world-class TPM discipline, manage cross-functional delivery, mitigate technical risk, and drive execution across product, engineering, design, and data science to achieve business impact in regulated environments.

Lead design, development, deployment, and support of foundational AI systems (LLM training/inference, similarity search, guardrails, observability). Optimize large-scale model performance and collaborate with cross-functional teams to deliver responsible, scalable AI products.

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 strategic advisory for M&A, divestitures, and restructurings; analyze market trends and risks; develop and execute deal and value-creation strategies; drive business development; maintain executive client relationships; mentor teams; promote AI and technology-driven value creation while ensuring quality, integrity, and compliance.

Lead deal-strategy engagements across M&A, divestitures, and restructuring. Analyze markets, build financial models, identify risks/opportunities, design value-creation strategies, manage projects, and mentor teams while leveraging technology and innovation to drive client outcomes.

Lead strategic advisory for mergers, acquisitions, divestitures, and restructuring. Analyze market trends, build strategic frameworks, identify risks and value creation opportunities, guide deal execution and post-merger integration, direct and coach teams, and communicate clear, impactful recommendations to clients and stakeholders.

Responsible for leading design projects, translating AI insights into intuitive interfaces, conducting user research, and collaborating with cross-functional teams to deliver innovative health-focused designs.

The role involves designing and operating AI systems, building data pipelines, fine-tuning models, and mentoring team members while collaborating with product and data science teams.

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 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.

Design and build AI-powered workflows and systems to transform operations across the company by partnering with stakeholders and shipping production-quality software from architecture to deployment.

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 Senior Solution Architect leads the architectural strategy for finance systems, aligning technology with business goals, ensuring compliance, and driving innovation through AI and ML integration.

As a Solution Consultant, you will help Life Sciences companies implement Veeva's Vault Safety platform, driving innovation and streamlining pharmacovigilance processes.

As a Solution Consultant, you will bridge pharmacovigilance challenges with Veeva's solutions, showcase value, build relationships, and innovate processes in the Life Sciences industry.

The co-op involves owning and delivering AI projects, conducting data analysis, building workflows, and communicating insights to stakeholders over a 6-month period.