Top AI Engineer Jobs in Boston, MA

Design, develop, deploy, and support scalable 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 production systems. Own complex AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews, collaborate across engineering and product teams, and mentor senior and principal associates.

Design, develop, deploy, optimize, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, design reviews, and technical roadmaps for scalable production AI. Optimize model performance, hardware utilization, latency, throughput, and cost while ensuring responsible AI standards. Mentor senior technical staff and collaborate with engineers, researchers, program managers, and product managers.

Develop and lead scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Design end-to-end architectures, optimize model performance and infrastructure utilization, define reliability objectives, ensure security and compliance, contribute to technical roadmaps, and mentor engineering staff. The role requires strong programming, AI/ML engineering, distributed systems, cloud, and model optimization expertise.

Design, develop, deploy, optimize, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance optimization across scalable AI platforms, establish engineering standards, contribute to long-term technical roadmaps, and mentor senior AI engineers.

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

Design, build, deploy, and maintain resilient, scalable backend services and platform components for Toast's Voice AI platform. Lead cross-cutting architecture and projects, integrate AI platforms (LLMs), implement observability and evaluation for AI systems, set engineering standards, and mentor engineers.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Leads AI and data-driven solutions for AML and sanctions clients in banking and capital markets. Responsibilities include developing AI solutions with Python, managing data architecture and pipelines, overseeing analytics and visualization deployments, guiding advanced analytics teams, engaging stakeholders, validating client outcomes, improving processes, and coaching diverse teams through complex situations.

The AI Application Engineer will design and deploy AI solutions, integrate them into cloud services, and collaborate with teams to enhance operational workflows.

Own production operations for workplace AI platforms, including configuration, integrations, observability, secrets, access controls, model and prompt changes, benchmarking, and incident response. Translate technical findings into governance, risk, compliance, and training materials while collaborating with Security, Legal, HR, and business stakeholders. Investigate failures, implement mitigations, document root causes, and maintain reliable, secure AI services.

Leads AI engineering and advanced analytics initiatives for AML and sanctions clients. Develops scalable data, visualization, and analytics solutions; validates data integrity, security, and compliance; identifies system linkages and optimization opportunities; manages client relationships and project execution; and mentors teams delivering innovative AI solutions.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

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.

Develop AI and data analytics solutions for AML and sanctions clients within PwC’s Risk & Regulatory practice. Responsibilities include Python-based data analysis, designing data architectures and pipelines, creating dashboards, interpreting complex datasets, engaging stakeholders, and delivering actionable insights and tailored client solutions. The role also involves client relationship building, problem-solving, continuous improvement, and contributing to strategic business outcomes.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Lead the technical and product direction for Klaviyo’s Customer Agent, building and scaling production conversational AI systems. Design orchestration, evaluation, guardrails, memory, context, and failure-handling capabilities; write and review code; resolve complex production issues; establish reusable engineering patterns; mentor engineers; and partner with product, design, data science, and go-to-market teams. The role requires hands-on ownership of reliable, secure, multi-tenant agentic systems operating against live customer traffic.

Design, develop, deploy, and support AI solutions including generative AI applications, agents, workflow automation, document intelligence, and retrieval-augmented generation. Partner with business and IT teams to identify use cases, integrate enterprise platforms and data sources, establish prompt and evaluation standards, support AI governance and security, monitor solution performance, and train employees on responsible AI adoption.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Designs, develops, tests, deploys, and supports foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with engineering, research, product, and program teams; contributes to technical vision and roadmaps; and mentors teams while applying emerging AI research to production systems.

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 the AI strategy, roadmap, architecture, and development of production-grade AI systems within a strategic business domain. Identify high-value opportunities, drive adoption and measurable business outcomes, establish evaluation, testing, guardrail, and observability practices, and mentor AI engineers and agent builders. Partner with product managers, business leaders, and end users to advance AI transformation while ensuring scalable, reliable, and maintainable solutions.