Top AI Jobs in Boston, MA

Own the end-to-end integration and automation architecture for the MyCDW platform. Design ServiceNow integrations, APIs, event-driven patterns, middleware, data contracts, AI-agent guardrails, automated workflows, and observability. Establish governance, security, coding standards, testing, documentation, and reusable architecture patterns. Evaluate platforms, lead prototypes, resolve complex issues, manage vendors, influence stakeholders, and align solutions with business strategy, compliance requirements, scalability, resilience, and cost objectives.

Build and lead enablement programs for Customer Success and Implementation teams, including onboarding, product training, continuous learning, playbooks, and AI-enabled workflows. Identify performance gaps, translate product updates into practical training, improve adoption of tools and processes, and establish feedback loops with Product, Support, Customer Education, and Product Marketing. The role supports stronger renewals, expansions, customer outcomes, and operational efficiency in a growing SaaS organization.

The Distinguished Engineer will drive innovation in banking technology, lead software engineering projects, mentor teams, and utilize AI and advanced architectures for risk management.

Leads engineering teams building responsible, scalable AI systems and products. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, observability, and production deployment. Makes build-versus-buy decisions across AI technologies, develops LLM optimization techniques, shapes foundational AI roadmaps, and recruits and mentors engineering talent. Partners with research, engineering, product, and program teams to deliver high-performance AI capabilities.

Design, develop, deploy, and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to long-term platform strategy, apply advanced AI research in production, and collaborate with engineers, scientists, program managers, and product leaders while mentoring teams.

Lead the design, development, deployment, and support of foundational AI systems, including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques that improve scalability, cost, latency, throughput, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to AI platform strategy, technical vision, and long-term roadmaps.

Lead the design, development, testing, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Collaborate with engineering, research, program management, and product teams while shaping the technical vision and roadmap for responsible AI platforms.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Establish technical vision and roadmaps, apply emerging research in production, and lead engineering teams while influencing cross-functional stakeholders.

In this role, you will lead AI projects, collaborating with cross-functional teams to develop and deploy advanced AI solutions, focusing on LLM inference and model optimization.

This role involves partnering with teams to develop AI-powered technology, advancing applied research in AI, and delivering impactful models. Candidates will work on foundational models and leverage new technologies to optimize customer interactions with banking services.

The Senior Lead AI Engineer will develop and support AI software components, partner with cross-functional teams, and enhance AI systems performance. Responsibilities include designing, deploying, and optimizing AI models for improved banking services.

Design and deliver end-to-end AI/ML solutions for Oncology R&D, working with multimodal datasets. Partner with scientists and product leaders to prototype, iterate, and deploy decision-relevant models, document methods for transparency, and ensure solutions are scientifically grounded and usable.

Lead design and prototyping of AI-enabled solutions for Oncology R&D. Develop data pipelines, feature engineering, analytics, and ML models on healthcare data. Partner with scientific and product teams to deliver usable tools, emphasize rapid iteration and production-readiness, and contribute to engineering standards and documentation.

Lead Oncology AI product strategy and a cross-functional AI engineering team to translate scientific needs into scalable AI solutions across discovery, clinical development, and real-world evidence. Define roadmaps, governance, success metrics, stakeholder engagement, and adoption to move AI initiatives from pilots to sustained impact.

As a DevOps Engineer, oversee infrastructure for AI systems, manage Kubernetes clusters, GitOps pipelines, deployment strategies, security posture, and platform reliability. Collaborate with feature teams to enhance developer experience and manage infrastructure costs.

As a Staff Product Manager, you'll drive the strategy and success of SIEM products, collaborating with various teams to fulfill customer needs and market demands while focusing on security for SMBs.

Lead and operationalize GitLab's Internal Audit function: plan and execute risk-based audits, oversee SOX testing, facilitate enterprise risk management, coach a distributed audit team, implement QA/improvement programs, and report to senior leadership and the Audit Committee.

Build and scale AI-ready data platforms for biotherapeutics research. Develop data products, machine learning and agentic AI pipelines, integrate public and proprietary datasets, and improve data governance, metadata, storage, visualization, and analysis. Collaborate with scientists, external companies, and academic institutions while supporting platform adoption through training and workshops. Publish and present scientific work and take solutions from prototype to production.

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 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 design and delivery of AI-driven P&C insurance operations solutions: analyze client needs, architect and implement AI/software solutions, manage projects and budgets, coach teams, train clients, ensure adoption, and maintain standards, governance, and evaluation practices to drive operational efficiency and innovation.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Lead design, build, deploy, and support GenAI platform services (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, collaborate cross-functionally, contribute to technical vision and roadmap.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.

Lead modernization of enterprise AI/ML infrastructure and analyst workflows. Build agentic, scalable, secure platform services, integrate generative AI and low-code/no-code tooling, scale AutoML for tabular and time-series, and engineer automated governance and controls while mentoring teams and influencing cross-functional stakeholders.