Top AI Engineer Jobs in Boston, MA

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Design and build NVIDIA’s AI data platform for multi-cloud GPU fleets. Responsibilities include high-throughput data paths, distributed caching, checkpoint management, Kubernetes-native storage integrations, quota and lifecycle systems, I/O telemetry, capacity forecasting, and production reliability. The role partners with AI researchers and supports workloads across Kubernetes and Slurm, requiring expertise in distributed storage, data infrastructure, I/O performance, and systems programming.

Design, build, and operate secure full-stack AI applications from prototype through production. Develop Angular and TypeScript frontends, Node.js/NestJS backends, and Python/FastAPI AI services. Deliver GenAI solutions using RAG, agents, embeddings, vector search, evaluation, guardrails, and human oversight. Own testing, CI/CD, observability, releases, incident response, and application support while partnering with product, architecture, cybersecurity, quality, and validation teams in regulated environments.

Lead design, build, and deployment of AI-enabled analytics using Microsoft AI technologies and cloud data platforms. Develop AI assistants, copilots, RAG and agentic workflows, integrate solutions with Power BI and semantic models, prototype and productionize with Python and SQL, implement governance and observability, and partner with BI, data engineering, and business stakeholders to scale enterprise AI use cases.

Build and scale enterprise AI capabilities, including multi-agent systems, prompt engineering, RAG pipelines, vector search, LLM integrations, APIs, and cloud deployments. Develop reusable platform components, monitor AI quality and performance, troubleshoot distributed systems, collaborate with data and platform teams, and translate business needs into reliable AI solutions. The role also requires documenting architecture and implementing governance, evaluation, and operational best practices.

Develop and deploy scalable, secure AI and machine learning solutions for business applications. Responsibilities include preparing datasets, building and evaluating models, monitoring deployed systems, optimizing performance, researching emerging AI technologies, and integrating AI capabilities with existing workflows. The role collaborates with data scientists, software engineers, and business stakeholders to deliver intelligent systems that improve efficiency and decision-making.

Architects and leads AI/ML development for surgical navigation and planning systems. Builds, optimizes, packages, versions, and deploys image-based models to on-device and cloud environments under real-time constraints. Defines ML APIs and data contracts, partners with software and cross-functional teams, supports regulatory documentation and validation, leads design and code reviews, contributes to Agile planning, and mentors engineers.

Build and deploy AI-powered tools for Rula’s business and clinical teams by observing workflows, rapidly delivering production software, measuring usage and impact, and enabling teams to operate tools independently. The role requires privacy-by-design practices, rigorous AI evaluation, customer-centric discovery, and integration with complex legacy systems. Staff engineers may optimize LLM costs and quality while creating reusable building blocks for future teams.

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

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.

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.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production AI platforms. Define technical standards across HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, reliability, observability, security, and governance. Solve complex performance and scalability challenges, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a highly regulated environment.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, reliability, and hardware utilization. Define system architectures and SLOs, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Lead the technical and product direction for a production conversational AI customer agent. Build and scale agentic systems covering orchestration, evaluation, guardrails, memory, context, reliability, and failure handling. Remain hands-on with coding, architecture, debugging, and production operations while mentoring engineers and partnering with product, design, data science, and go-to-market teams. The role is based in-office in Boston and may require up to 10% travel.

Lead the technical and product direction for a production conversational AI customer agent. Build and scale agentic systems involving orchestration, evaluation, guardrails, memory, context, reliability, and failure handling. Write and review code, make architecture decisions, collaborate cross-functionally, mentor engineers, evaluate emerging AI technologies, and support systems through launch and live customer usage.

Designs, develops, deploys, and optimizes production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role establishes AI cost-performance standards, leads technical design reviews, contributes to platform roadmaps, optimizes model and hardware utilization, and mentors senior AI engineers while delivering scalable and responsible AI capabilities.

Develop and operate large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, guardrails, model evaluation, vector search, memory, governance, and observability. Lead architecture and optimization for cost, latency, throughput, reliability, and hardware utilization. Define AI service objectives, ensure security and compliance, guide technical roadmaps, and mentor engineering teams.

Designs, develops, deploys, and supports production AI systems, including foundation models, LLM inference, agentic workflows, guardrails, evaluation, and observability. Owns complex AI architecture, reliability objectives, governance, and optimization for cost, latency, throughput, and hardware utilization. Partners across engineering, research, product, and program teams; leads technical reviews; contributes to long-term platform strategy; and mentors senior technical associates.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, and multi-model orchestration. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads governance reviews and design councils, contributes to technical roadmaps, establishes responsible AI standards, and mentors senior engineering leaders.

Design, develop, deploy, and support large-scale 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. Lead technical reviews and governance, define AI platform roadmaps, integrate heterogeneous AI systems, and mentor senior engineering talent.

Design, develop, deploy, and support large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Establish governance and engineering standards, lead design reviews, contribute to platform strategy, and mentor senior AI engineers.

Three-month AI engineering R&D internship developing and evaluating machine learning and computer vision solutions for future sports medicine technologies. Responsibilities include creating algorithms for medical imaging and navigation, integrating software into the TESSA ecosystem, prototyping AI-enabled workflows, analyzing data, defining metrics, documenting results, and presenting findings to engineering and R&D teams.

Build and deploy AI-powered workflows, agents, automations, and internal tools across business teams. Partner with teams to redesign processes, integrate AI with enterprise systems, establish governance, access controls, evaluations, monitoring, and cost tracking. Evaluate AI tooling, create reusable enablement patterns, train employees, document solutions, and promote self-service agent development while ensuring secure, permission-aware operation.

Designs, develops, deploys, and maintains production AI systems for construction workflows. Builds RAG pipelines, multi-model and multi-agent architectures, backend APIs, data integrations, and LLMOps capabilities using AWS, Databricks, Python, and related technologies. Automates infrastructure and deployments, monitors performance and costs, and collaborates with product, data, site AI, and business teams.