Top AI Engineer Jobs in Boston, MA

Develop and deploy 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, scalability, and reliability. Own complex AI architecture, define reliability objectives, improve GPU/TPU utilization, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.

Design, develop, deploy, and support enterprise AI systems, including foundation models, LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Establish AI architecture standards, optimize model performance, cost, latency, and scalability, and lead model routing and orchestration initiatives. Drive responsible AI practices, contribute to technical roadmaps, mentor engineering teams, and influence cross-functional stakeholders and enterprise AI strategy.

Develop and deploy AI capabilities across enterprise ERP platforms including Workday and SAP Ariba. Build intelligent agents, chatbots, automation, integrations, and custom GenAI/LLM solutions. Establish PaaS environments, cloud services, deployment pipelines, security, governance, and engineering processes. Lead AI projects from concept through production, evaluate vendor versus custom solutions, support technology roadmaps, and promote AI literacy. Provide production support and training while aligning solutions with business needs and measurable ROI.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead architecture, cost-performance governance, technical reviews, and long-term roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI standards. Mentor senior technical leaders and collaborate with engineers, scientists, technical program managers, and product managers.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability SLOs, and technical roadmaps while collaborating across engineering, research, infrastructure, and product teams. Lead deployment reviews for security and compliance and mentor senior technical staff.

Develop and deploy scalable AI systems, including foundation models, LLM inference, agent workflows, vector search, guardrails, evaluation, and governance. Optimize model performance for cost, latency, throughput, and accuracy; build training and fine-tuning pipelines; collaborate with research and data engineering teams; and mentor junior engineers while advancing responsible AI practices.

Design, develop, optimize, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization for cost, latency, throughput, and hardware utilization; establish AI engineering standards; guide technical roadmaps and design reviews; and mentor senior AI engineers.

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. Define AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews and mentor engineering teams across complex AI product initiatives.

Leads enterprise AI and software engineering modernization initiatives, designing, building, testing, deploying, and maintaining scalable cloud solutions. Establishes engineering standards, applies generative AI development practices, reviews complex architectures, resolves technical issues, and collaborates with infrastructure, security, operations, leadership, and external partners. Provides technical influence, mentorship, and strategic guidance while supporting cloud migration and automation across the organization.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.

Designs, develops, deploys, and optimizes large-scale generative AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role leads architecture and cost-performance optimization, establishes AI engineering standards, contributes to platform roadmaps, and mentors senior technical staff. It requires deep expertise in AI systems, software engineering, cloud platforms, model optimization, and responsible AI deployment.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, GPU/TPU utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to foundational AI roadmaps, and mentor engineers on scalable design and research-to-production practices.

Designs, develops, deploys, and supports large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model evaluation. Defines enterprise AI architecture, optimizes model performance, cost, latency, and throughput, establishes AI safety and governance standards, leads multi-year platform initiatives, and mentors senior technical leaders across engineering and research.

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.

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.

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.