Top AI Jobs in Boston, MA

Lead design and implementation of AI and blockchain-enabled integration and platform architectures. Analyze requirements, develop technical designs, direct large projects, validate outcomes with clients, coach diverse teams, drive cloud strategy (Azure), perform feasibility studies and business process modeling to optimize enterprise technology infrastructure.

Lead the design, development, deployment, and governance of scalable AI systems, including foundation models, LLM inference, similarity search, guardrails, evaluation, and observability. Define AI platform strategy, optimize production systems, establish Responsible AI standards, and translate enterprise priorities into execution plans. Build and mentor multi-team engineering organizations while partnering with research, product, compliance, and risk teams.

Design, develop, deploy, and optimize large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, orchestration pipelines, and observability. Lead cost-performance governance, technical design reviews, AI standards, roadmap development, and mentorship while improving model scalability, latency, throughput, hardware utilization, and inference costs.

Design, develop, deploy, and support scalable generative AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across distributed cloud platforms. Own AI system architecture and reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term platform roadmaps, and mentor engineering staff.

Leads the architecture, development, deployment, and optimization of large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes enterprise AI architecture, performance and safety standards, and multi-year platform strategies. Partners across engineering, research, product, and program management while mentoring technical leaders and driving responsible AI adoption.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability objectives, governance, security, and compliance while collaborating across engineering, research, product, and program teams. Lead technical reviews, shape foundational AI roadmaps, and mentor senior technical staff.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Design, develop, optimize, deploy, and support foundational AI systems, including LLMs, agents, vector search, model evaluation, orchestration pipelines, and responsible AI controls. Lead cost-performance governance, technical design reviews, roadmap development, and optimization of model and hardware utilization. Mentor senior AI engineers while partnering with research, product, program, and engineering teams to deliver scalable production AI platforms.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, guide hybrid and multi-cloud strategies, and promote responsible AI practices. Lead technical direction, mentor engineering teams, collaborate with cross-functional stakeholders, and advance research techniques into production.

Conduct applied research on large language models, training optimization, and fine-tuning. Design, train, evaluate, validate, and deploy AI foundation models using PyTorch, AWS, Hugging Face, and Lightning. Lead research initiatives across data science, engineering, and product teams, translating advances into scalable production systems. Candidates should have advanced academic training and experience with large-scale deep learning, model optimization, self-supervised learning, robustness, explainability, or RLHF, plus a record of impactful research and publications.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, collaborate across engineering and research teams, and mentor senior technical staff.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Define reliability objectives, lead architecture and technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor senior technical staff.

Develop and deploy scalable AI systems, including foundation models, LLM inference, agent workflows, similarity search, guardrails, evaluation, and observability. Optimize model training and inference for cost, latency, throughput, and scalability. Build training and deployment pipelines, improve datasets and evaluation methods, support governance and security, contribute to AI platform roadmaps, and mentor junior engineers.

Lead the architecture, development, deployment, optimization, governance, and observability of scalable AI platforms and systems. Build foundation model training, LLM inference, agentic workflows, similarity search, guardrails, and evaluation capabilities. Establish enterprise AI safety and transparency standards, drive long-term infrastructure strategy, mentor technical leaders, and guide organization-wide adoption of responsible AI solutions.

Design, develop, deploy, optimize, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and orchestration pipelines. Lead cost-performance governance, technical design reviews, AI engineering standards, and long-term platform architecture. Apply state-of-the-art optimization techniques for latency, throughput, scalability, hardware utilization, and inference costs while mentoring senior technical staff and ensuring responsible AI practices.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across scalable production systems. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering staff.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and observability. Lead multi-model orchestration, cost-performance governance, technical design reviews, and AI platform roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while mentoring senior technical teams and promoting responsible AI standards.

Conduct applied research and develop large-scale AI foundation models, including vision-language models, across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver AI-powered products. Research optimization, self-supervised learning, robustness, explainability, and fine-tuning methods, while producing technical reports and conference submissions.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish performance governance, lead technical design reviews, shape long-term platform strategy, and mentor senior AI engineering leaders.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, collaborate across engineering and product teams, and mentor senior technical staff.

Design, build, deploy, and support enterprise AI systems including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for scalability, cost, latency, throughput, and hardware utilization. Establish enterprise AI architecture standards, lead multi-cloud strategies, mentor engineering teams, promote responsible AI, and influence long-term technical roadmaps.

Leads Dynatrace’s enterprise AI strategy, roadmap, governance, and value realization across global business functions. Evaluates AI technologies, vendors, platforms, and build-buy-partner decisions; establishes responsible AI policies and risk controls; and builds AI enablement programs and a Center of Excellence. Advises executives, measures productivity and ROI, drives organizational adoption, and guides enterprise AI architecture and deployment reliability.

Lead the technical direction and execution of WHOOP’s foundation AI modeling efforts across wearable sensor data, language, biomarkers, and behavioral signals. Drive high-risk research from hypothesis to production, shape AI strategy and infrastructure decisions, collaborate across research, product, and engineering, mentor senior technical talent, establish evaluation standards, and represent WHOOP externally through recruiting, partnerships, and the machine learning research community.