Top Tech Jobs & Startup Jobs in Boston, MA

Build, deploy, and maintain production machine learning models and infrastructure. Design ML components, optimize algorithms, create data pipelines, operate distributed systems, and manage cloud-based containerized services. Apply testing, CI/CD, monitoring, model governance, responsible AI, and explainability practices while collaborating with product, data science, and cross-functional engineering teams.

Leads engineering teams and portfolios delivering distributed, cloud-native, full-stack systems for risk management. Designs architectures, develops microservices and APIs, guides platform roadmaps, and deploys reliable solutions using cloud technologies, databases, containers, and multiple programming languages. The role also includes mentoring engineers, shaping technical standards, applying observability and reliability practices, collaborating with product and architecture leaders, and leveraging AI technologies and tools.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, collaborate across engineering and product teams, and mentor senior technical staff.

Design, build, deploy, and support enterprise AI systems including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for scalability, cost, latency, throughput, and hardware utilization. Establish enterprise AI architecture standards, lead multi-cloud strategies, mentor engineering teams, promote responsible AI, and influence long-term technical roadmaps.

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

Build, deploy, scale, and monitor production machine learning models and infrastructure for risk management. Responsibilities include model development, data pipeline construction, cloud and distributed systems operation, Kubernetes-based deployment, CI/CD, model governance, responsible AI, testing, monitoring, and collaboration with product and data science teams.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, 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 performance governance, lead technical design reviews, shape long-term platform strategy, and mentor senior AI engineering leaders.

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 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.