Top AI Jobs in Boston, MA

Develop and deploy foundational AI systems, including LLM inference, foundation model training, 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 technical standards and governance, lead design reviews, contribute to long-term AI roadmaps, and mentor senior AI engineering staff.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, and observability. Optimize training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability objectives, governance, and technical roadmaps while collaborating cross-functionally and mentoring engineering teams.

Design, develop, deploy, and support foundational AI systems, including large language model inference, foundation model training, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Own complex system architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, scalability, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering talent.

Build and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and orchestration pipelines. Lead cost-performance governance, improve model scalability and hardware utilization, shape technical roadmaps, establish AI engineering standards, and mentor senior technical staff. The role requires applying current AI research to reliable, responsible production systems across cloud and large-scale infrastructure.

Leads an AI engineering team developing, deploying, and supporting production AI systems, including foundation models, LLM inference, similarity search, guardrails, evaluation, observability, and responsible AI governance. Owns technical strategy, optimization, build-versus-buy decisions, roadmaps, team development, and cross-functional collaboration. The role also applies emerging AI research to scalable, reliable, cost-effective production systems.

Conduct applied research and develop large-scale AI foundation models, particularly LLMs, optimization methods, and fine-tuning techniques. Responsibilities include designing, training, evaluating, validating, and deploying models; optimizing training and inference at scale; collaborating with data scientists, engineers, and product managers; translating research into business applications; and producing technical reports or conference publications.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads engineering teams delivering scalable AI and data capabilities for Fidelity Wealth. Sets technical direction across architecture, platforms, security, scalability, resiliency, and observability. Builds and develops high-performing engineering organizations while partnering with product, business, data, and technology stakeholders to translate strategy into measurable delivery plans. Requires deep software engineering expertise spanning APIs, data modeling, customer applications, cloud solutions, distributed systems, serverless architectures, event-driven systems, and microservices.

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

Design, develop, deploy, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, lead technical governance and design reviews, define AI engineering standards, and mentor senior technical staff.

Design, build, deploy, and support scalable AI systems, including foundation models, LLM inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor senior technical staff.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, AI standards, and design reviews while mentoring senior technical staff. Partner with research, engineering, product, and program teams to establish foundational AI roadmaps and deliver scalable, responsible systems.

Designs, develops, deploys, and supports large-scale AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for scalability, latency, throughput, cost, and hardware utilization; defines reliability objectives; leads architecture and technical reviews; and mentors engineering staff.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture, cost-performance optimization, technical standards, and design reviews for scalable production AI systems. Mentor senior technical staff and contribute to the long-term roadmap for responsible AI infrastructure.

Conduct applied research and build AI foundation models, including large language and agentic AI systems, through design, training, evaluation, validation, and deployment. Partner with data scientists, engineers, and product managers to translate research into scalable customer-facing products. Lead research initiatives, investigate state-of-the-art methods, optimize large-model training and inference, and guide junior researchers or engineers.

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research LLM pretraining, fine-tuning, reinforcement learning, robustness, explainability, and behavioral models. Develop platform or solution-level code, reproduce research, document experiments, and contribute to technical reports or conference submissions.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimizes large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads architecture and design reviews, establishes AI engineering standards, drives technical roadmaps, and mentors senior AI engineers.

Design, develop, optimize, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization of production AI systems for scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, establish governance standards, lead design reviews, contribute to technical roadmaps, and mentor senior AI engineering staff.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and optimize foundation models through design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products, translate research into business outcomes, and publish technical findings. Requires advanced research expertise in deep learning, LLMs, optimization, fine-tuning, data preparation, and reproducible experimentation.

Design, develop, deploy, and support production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, lead AI governance and technical design reviews, establish engineering standards, and mentor senior technical staff.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, foundation model training, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Owns complex AI architecture, defines reliability objectives, leads technical reviews, ensures security and compliance, contributes to long-term technical roadmaps, and mentors senior and principal engineers.

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

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models, optimize training and inference, develop scalable machine learning solutions, and translate research into production systems. Lead cross-functional research initiatives with data scientists, engineers, and product managers while independently pursuing impactful research agendas. The role emphasizes deep learning, NLP, model fine-tuning, robustness, explainability, and deployment at scale.