Top Tech Jobs & Startup Jobs in Boston, MA

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.

Manage the development, implementation, governance, and adoption of enterprise ontologies and optimized data models. Partner with Finance, Technology, Product, Machine Learning, and enterprise teams to support ERP replacement, semantic technology integration, data standardization, and data harmonization. Own the ontology roadmap, establish standards using W3C technologies, communicate strategic vision, manage and mentor teams, and advocate for semantic technologies across the organization.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, machine learning specialists, and product managers to deliver scalable AI-powered products. Research emerging methods, translate technical findings into business outcomes, develop platform and solution code, and contribute to technical reports or conference submissions. The role focuses on large deep learning models, optimization, fine-tuning, robustness, explainability, and research agenda ownership.

Leads full-stack software engineering teams and technology portfolios supporting risk and GRC solutions. Responsibilities include managing developers, setting technical strategy, driving cloud-native distributed-system design, mentoring engineering leaders, partnering with product and architecture teams, defining roadmaps, and delivering large-scale initiatives. The role involves JavaScript and TypeScript ecosystems, multiple programming languages, AWS and other cloud platforms, microservices, databases, Kubernetes, AI-enabled solutions, and enterprise technical governance.

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.

Conduct applied AI research and develop foundation models from design through training, evaluation, validation, and production deployment. Lead research initiatives spanning large-scale deep learning, language models, optimization, fine-tuning, robustness, and explainability. Partner with data scientists, engineers, and product managers to translate research into scalable AI products, while guiding junior researchers and advancing research agendas.

Leads multiple technology projects and engineering teams building large-scale, distributed, cloud-native systems. Provides architectural and technical leadership, establishes engineering standards, shapes platform roadmaps, and delivers reliable full-stack solutions. Collaborates with product managers, architects, and technology leaders while mentoring engineers and driving enterprise-wide technical strategy. The role uses modern programming languages, cloud platforms, databases, containers, observability, CI/CD, and AI-assisted development tools.

Leads multiple technology projects and engineering teams building large-scale, distributed, cloud-native full-stack systems. Provides technical leadership on architecture, platform roadmaps, reliability, performance, observability, coding standards, and enterprise technology strategy. Collaborates with product managers, architects, and cross-functional partners while mentoring engineers and delivering secure solutions using modern programming languages, cloud platforms, microservices, databases, containers, and CI/CD practices.

Leads data science for enterprise agentic AI products, developing and deploying machine learning models, designing experiments, analyzing telemetry, and translating findings into business strategy. Partners with data scientists, AI/ML engineers, and product managers while guiding large-scale analysis across numeric and textual data. The role also requires technical leadership, stakeholder communication, innovation, talent development, and expertise in statistical modeling, deep learning, and cloud-based data science.

Build and architect scalable cloud data platforms, pipelines, and applications. Design distributed, real-time, and batch data solutions using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.