Top AI Jobs in Boston, MA

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.

Lead design and delivery of conversational and agentic AI solutions for client contact centers. Manage teams, mentor junior staff, gather requirements, design UIs, run usability testing, and implement AI integrations to improve customer experience and product adoption.

Lead design and delivery of conversational and agentic AI solutions for client contact centers. Guide cross-functional teams to build NLP interfaces, prototype UIs, run usability testing, ensure data integrity and security, and advise clients strategically. Coach teams, manage large projects, and drive adoption of AI-powered contact center technologies to improve client experience and operational outcomes.

Lead Microsoft Dynamics 365 CRM implementations, architect solutions, manage project lifecycles and client accounts, mentor teams, ensure quality and post-go-live support, and drive innovation and continuous improvement.

Lead AI governance projects within Risk and Compliance, assess governance/risk/control programs, translate technical concepts for non-technical stakeholders, design and evaluate AI governance frameworks and controls, interpret data to inform recommendations, build client relationships, and mentor junior team members.

Lead design and implementation of enterprise AI and cloud architectures, manage client engagements and project delivery, mentor teams, evaluate new technologies, and ensure adherence to quality and professional standards.

Lead development and implementation of advanced AI/ML solutions, design and optimize models and algorithms, transform raw data into actionable insights, manage projects end-to-end, supervise and mentor teams, maintain quality and standards, and manage client engagements and expectations.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead M&A, divestiture, and restructuring advisory engagements; analyze market trends and financials; develop strategic frameworks; guide deal value creation and post-merger integration; coach and direct teams; communicate recommendations to clients and stakeholders.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

Lead development and delivery of AI and GenAI solutions for health systems. Serve as strategic advisor, manage teams, ensure HIPAA-compliant deployments, implement MLOps and model governance, translate clinical workflows into AI use cases, and drive stakeholder alignment and operational excellence.

Lead AI-focused product lifecycle from definition through release and end-of-life. Collaborate with cross-functional teams, perform competitive and customer needs analysis, build business cases and go-to-market plans, translate data into product insights, and manage stakeholder relationships to drive AI product adoption and business growth.

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

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.

The AI Product Manager oversees AI initiatives, translating business goals into actionable plans, collaborating on product delivery, and ensuring data integrity and compliance.

The Applied AI & Analytics Associate will conduct high-impact analytical work across functions, utilizing AI and data to drive business decisions and improvements, while collaborating with senior stakeholders and building analytical tools and models.

Design and deploy generative-AI workflows to accelerate 2D/3D product concepting and prototyping, build custom AI assistants and prompt frameworks, train cross-functional teams, and integrate AI-driven experiences across physical and digital product ecosystems.

Design, build, deploy, and support large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, and observability. Collaborate cross-functionally, optimize model performance and cost, and contribute to technical vision and roadmap for foundational AI platforms.

The role involves designing, developing, and supporting AI software components, leveraging various AI technologies, and optimizing performance for large-scale AI systems.

The role involves architecting a scalable AI platform, building distributed compute solutions, optimizing technologies, and mentoring internal talent in AI and ML.

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.

As a Senior Lead AI Engineer, you will develop AI solutions, partner with cross-functional teams, optimize LLM performance, and contribute to the AI vision at Capital One.

Lead design and delivery of MassMutual's AI platform components (LLM gateway, model serving, multi-tenant compute). Own architecture, technical execution, reliability (SLOs/observability), governance controls, and cross-team alignment. Provide technical leadership through design reviews, ADRs, and hands-on pairing.

Lead and manage cross-firm AI in Operations program initiatives: develop strategic plans, apply structured problem-solving and design thinking, support multiple workstreams, engage senior stakeholders, standardize AI use cases, and drive scaling and reimagination of AI capabilities across operations.

The AI Engagement Manager will lead strategic relationships with clients to implement AI solutions, driving business transformation and optimizing operations in the housing sector.