Top AI Jobs in Boston, MA

The AI Engineer designs and deploys AI automation tools, partners with teams for workflow automation, and enhances operational efficiency using generative AI and data-driven decision support.

Lead AI workforce strategy and org transformation for Product Development & Engineering: workforce planning, org design, job architecture, workflow redesign, change enablement, stakeholder advisement, and measurement of AI adoption and impact.

Design and run AI-first onboarding, enablement, and learning programs for Klaviyo engineering. Partner with Dev Infra, L&D, Recruiting, and Eng Leadership to operationalize AI in workflows, codify engineering practices, run experiments, measure impact, and scale training to improve developer velocity, quality, and satisfaction.

Partner with business teams to identify high-leverage AI opportunities; design, build, and ship agentic AI solutions and internal AI platform components. Deliver production LLM-based agents, integrations, and CI/CD pipelines while driving AI enablement, safety, and adoption across the company.

Lead a cross-organizational program to transition engineering to an AI-driven, self-optimizing ecosystem with robust telemetry and evaluation. Orchestrate strategy and execution, mitigate technical risk, build and grow a TPM practice, align product/engineering/design/data science, and deliver measurable business impact.

Lead delivery of AI-driven property & casualty insurance operations solutions: analyze client needs, design and implement software and AI architectures, manage projects and teams, coach staff, run training, ensure adoption, and drive process innovation while maintaining governance and standards.

Lead design and delivery of enterprise AI solutions including architecture, LLM integration, RAG, data pipelines, cloud deployment and governance. Oversee multiple projects, maintain executive client relationships, drive business development, mentor teams, and promote innovation across the PwC Network.

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

As an Applied Researcher II, you'll develop AI models, collaborate across teams, conduct applied research, and drive innovations in AI to improve customer experiences.

The role involves leading AI solutions development, optimizing AI systems, and collaborating with cross-functional teams to enhance customer interactions at Capital One.

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.

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.