Top AI Jobs in Boston, MA

The Applied Researcher I role involves developing AI foundation models, collaborating with cross-functional teams, and conducting advanced research to enhance banking experiences with AI.

The Enterprise Architect will design scalable AI solutions, mentor teams, support business development, and manage client relationships while evolving technology practices.

Lead design, development, deployment, and support of foundation model training and LLM inference platforms. Build similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, and scalability; contribute to technical vision and roadmap while partnering across engineering, research, product, and program teams.

As a Lead AI Engineer, you will design, develop, and support AI software components while partnering with cross-functional teams to deliver AI-powered products at Capital One.

The Senior Lead AI Engineer will develop and support AI components, lead a cross-functional team, and drive innovation in AI systems at Capital One.

The Senior Data Scientist will design and implement AI-enabled products using clinical trial data, mentor team members, and collaborate with stakeholders.

Lead commercialization and GTM for Mastercard's U.S. Mid-Market Accelerator partner-led solutions. Define regional strategy, identify and structure partners, secure approvals, drive cross-functional execution, manage contracts and Salesforce pipeline, monitor post-launch performance, and enable internal teams and issuers to accelerate adoption and revenue growth.

Lead and grow an AI engineering team to design, build, deploy, and support production-scale generative AI systems. Oversee foundation model training, LLM inference, vector search, guardrails, evaluation, governance, and observability. Make build-vs-buy decisions across open source and SaaS, optimize LLM performance for cost/latency/throughput, set technical vision and roadmap, and attract/mentor top AI engineering talent.

Lead and build an Enterprise AI organization to define strategy, platforms, governance, and standards. Deliver scalable AI platforms (LLM gateways, model registries, vector DBs), enable business adoption, embed responsible AI and compliance, partner across Security, Legal, Finance, and drive measurable AI use cases and ROI while managing a small, cross-functional AI architecture and engineering team.

Lead deployment, upkeep, and innovation of factory digital use cases (data capture, analysis, prediction) to improve productivity, quality, safety, and cost. Coordinate mitigation with departments, mentor leaders and operators, implement instrumentation/OT solutions (PLC/DCS/SCADA/HMI), use data visualization and predictive tools, and drive digital transformation tied to factory scorecards.

Own AI-focused software product lifecycle from definition through release and end-of-life. Collaborate with cross-functional teams to conduct market and customer analysis, develop business cases and go-to-market plans, apply analytics and business process modeling, prioritize AI product backlogs, and manage stakeholder relationships to drive commercialization and growth.

Use advanced analytics, statistical methods, and machine learning to transform large datasets into actionable insights. Build data solutions and pipelines, perform exploratory analysis, create visualizations, validate models (including generative AI and NLP), collaborate with clients, mentor juniors, and maintain data quality to inform strategic decisions.

Lead design and delivery of enterprise AI and cloud solutions: analyze client requirements, develop technical designs, implement Azure-based architectures, lead project planning and execution, mentor teams, evaluate new technologies, and maintain professional standards while building client relationships.

Lead design and delivery of AI and blockchain-enabled integration and platform architectures. Analyze requirements, create technical designs and cloud strategies (Azure), direct large projects, validate outcomes with clients, coach and manage diverse teams, perform feasibility and business process modeling, and drive enterprise architecture and system integration initiatives to support audit automation and strategic technology roadmaps.

Lead QA for AI-enabled software: design and execute test strategies, identify and resolve defects, perform functional/compatibility and model-output testing, automate tests (e.g., Selenium), analyze data to inform recommendations, support post‑go‑live issues, and mentor junior team members while managing client expectations.

Lead data science initiatives within Data and Analytics Engineering to build predictive models, maintain data pipelines and lakes, apply ML/DL and generative AI, ensure data quality, coach teams, manage project timelines, and collaborate with stakeholders to drive data-driven business growth.

Design, code, and test enterprise applications using Kotlin, Rust, and SwiftUI. Implement ATDD and CI/CD, API management and microservices, manage application lifecycle, perform code reviews and debugging, collaborate with clients, and drive business process improvements.

Lead and manage a software engineering team to design, build, test, and deploy AI-enabled, scalable applications. Own project planning, budgeting, delivery, code quality, CI/CD, observability, and cross-functional collaboration while coaching engineers and driving innovation.

Lead an AI engineering team to design, build, deploy, and optimize large-scale generative AI systems. Oversee foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and make strategic build-vs-buy decisions. Drive technical vision, roadmap, and talent development while ensuring scalable, cost-efficient, and responsible AI production systems.

Lead design, develop, deploy, and support foundational AI systems (LLM training/inference, similarity search, guardrails, observability). Partner cross-functionally to optimize scalability, cost, latency, and throughput, apply state-of-the-art research to production, and shape long-term AI platform roadmap.

Lead US TMT business development with an AI focus: identify revenue opportunities from AI regulation, M&A, governance and data trends; develop client strategies, communications and targeted outreach; support pitches and major US TMT projects; coach and manage MBD colleagues; collaborate with partners and international teams to drive client relationships and revenue growth.

Support venture finance team with quarterly US GAAP reporting and audits, tax-basis book prep, cash management, capital calls, and portfolio financial analysis. Prepare valuations, investor materials, respond to LP inquiries, ensure partnership compliance, manage fee and expense allocations, and assist on projects including AI implementation and other ad-hoc responsibilities.

The Head of Data Governance will establish frameworks for data integrity and compliance, oversee patient data anonymization, and lead governance initiatives. This role requires collaboration with senior leaders to ensure data strategy aligns with regulatory requirements and business goals.

Lead policy strategy on blockchain, digital assets, and emerging technologies; advise cross-functional teams on regulatory, risk, and policy implications; engage regulators and industry groups; translate complex technical concepts for senior leaders and stakeholders to inform decision-making.

The Director, Data Science oversees data-driven solutions, manages the Data Science team, and collaborates with business units and product development to implement advanced techniques. Provides strategic leadership and mentorship to enhance team effectiveness.