Top Tech Jobs & Startup Jobs in Boston, MA

Leads technical vision and software architecture across Capital One, designing scalable cloud, data, microservice, and event-driven solutions. Drives modernization, defines platform and API standards, ensures technical quality, leads cross-divisional initiatives, and mentors engineers. Requires deep experience in software engineering, enterprise architecture, cloud computing, data architecture, distributed systems, web technologies, databases, networking, and full lifecycle delivery.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Build, deploy, scale, and maintain machine learning models and infrastructure for production use. Responsibilities include designing ML solutions, optimizing models and data pipelines, operating distributed systems and cloud platforms, automating testing and deployment, monitoring production models, and applying responsible AI and software development practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Leads data science initiatives for customer financial resiliency, partnering with engineers and product managers. Builds, validates, implements, and productionizes supervised and reinforcement learning models across large-scale data. Uses Python, cloud platforms, open-source machine learning tools, and statistical methods to deliver personalized customer solutions. Translates complex analyses into business outcomes, evaluates emerging technologies, and develops team talent.

Build and lead development of cloud-based, full-stack risk-management solutions using distributed microservices, AI technologies, databases, and modern open-source frameworks. Provide technical leadership across Agile teams, define service architectures, improve system performance, reduce technical debt, establish engineering standards, mentor developers, and influence long-term technology strategy. Work with product managers, architects, and cross-functional partners to deliver secure, scalable solutions supporting regulatory needs.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Design, develop, deploy, and support scalable 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. Define AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews and mentor engineering teams across complex AI product initiatives.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.

Build and lead full-stack software engineering initiatives using distributed microservices, cloud platforms, APIs, databases, and modern development frameworks. Lead technology projects and developers, define architecture and service boundaries, improve system performance, reduce technical debt, establish engineering standards, mentor engineers, and deliver secure, scalable solutions in an Agile environment.