Top Tech Jobs & Startup Jobs in Boston, MA

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.

Lead consumer growth and retention product experiences for Capital One Shopping. Own reward and activation strategy, personalization, product journeys, business cases, prioritization, analytics, operational reporting, and investment decisions. Partner closely with engineering to rapidly build, A/B test, and optimize digital experiences. Use SQL, Excel, financial modeling, user behavior insights, and AI-enabled approaches to drive product adoption, activation, retention, and business growth.

Conduct applied AI research and develop foundation models from design through training, evaluation, validation, and production deployment. Collaborate with data scientists, software engineers, machine learning engineers, and product managers to deliver scalable AI products. Lead research initiatives, evaluate emerging methods, optimize large-scale deep learning systems, and translate scientific findings into customer and business impact.

Leads full-stack software engineering teams and portfolios, setting technical strategy, mentoring engineering leaders, and delivering large-scale distributed and cloud-native systems. Responsibilities include managing Agile teams, defining technology roadmaps, partnering with product and architecture leaders, driving enterprise alignment, overseeing architecture and implementation, and supporting succession planning. The role uses modern programming languages, cloud platforms, databases, containers, CI/CD, and automated testing while occasionally coding or reviewing code.

Leads full-stack engineering teams and a portfolio of technology projects within Risk Tech. Responsibilities include setting technical strategy, mentoring engineering leaders, delivering cloud-native distributed systems, partnering on roadmaps, managing multi-team Agile programs, and aligning engineering execution with enterprise goals. The role involves technologies including Java, Python, JavaScript, TypeScript, AWS, Docker, Kubernetes, databases, and AI-powered solutions.

Leads diverse technology projects and developer teams building cloud-based, full-stack risk management solutions. Provides technical direction across Agile teams, defines architecture and service integrations, mentors engineers, establishes engineering standards, and drives system performance and technical debt reduction. Develops distributed microservices and APIs using modern programming languages, databases, containers, AWS services, AI tools, CI/CD, and observability practices.

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.

Leads engineering teams and portfolios delivering large-scale, cloud-native risk management systems. Responsibilities include designing distributed microservices and full-stack solutions, guiding architecture and technology strategy, mentoring engineers, collaborating with product and architecture leaders, and building AI-powered regulatory and risk tools. The role uses multiple programming languages, cloud platforms, databases, containers, APIs, observability, CI/CD, and infrastructure-as-code practices.

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.

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.