Top Tech Jobs & Startup Jobs in NYC, NY

Leads cybersecurity product strategy, roadmaps, and enterprise security capabilities using threat, risk, data, and design thinking. Partners with engineering, operations, vendors, and executives to prioritize solutions, make build-versus-buy decisions, define metrics, and scale products. Oversees cybersecurity projects, risk programs, control automation, incident analysis, and stakeholder communication while recruiting, mentoring, and managing technical teams.

Leads engineering projects and developers while architecting, building, and maintaining resilient full-stack systems and backend microservices for real-time fraud detection. The role involves cloud development, API design, distributed systems, technical strategy, refactoring, operational excellence, mentoring, Agile leadership, and adoption of emerging technologies including AI coding tools.

Leads enterprise data engineering strategy and architecture for pipelines, external data sharing, and scalable data platforms. Designs and codes critical data solutions while driving adoption of modern technologies, governance, resiliency, operational excellence, and unified data architecture. Advises stakeholders, mentors engineers, recruits talent, evaluates emerging data and AI technologies, and develops platform roadmaps. The role requires hands-on expertise across AWS, lakehouse architecture, streaming, distributed computing, data warehousing, and multiple programming languages.

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.

Build and lead development of an agentic orchestration layer for Capital One’s marketing platform. Design distributed microservices, full-stack systems, cloud solutions, service boundaries, integrations, and reusable frameworks. Provide technical leadership across Agile teams, mentor engineers, establish engineering standards, reduce technical debt, optimize distributed-system performance, and evaluate emerging technologies. The role requires hands-on development across multiple programming languages, databases, cloud platforms, containers, APIs, CI/CD, infrastructure as code, and automated testing.

Build and lead development of native iOS mobile applications for large-scale customer experiences. Provide technical leadership in mobile architecture, API design, reusable components, testing, performance, accessibility, feature flags, staged rollouts, BFFs, and Server-Driven UI. Mentor engineers, establish engineering best practices, and promote AI-assisted development. Collaborate with software engineers, designers, and product managers in Agile teams to deliver high-quality mobile products.

Develops and enhances B2B trade credit product capabilities and customer servicing platforms. Partners with operations stakeholders, engineers, designers, data scientists, and business teams to identify opportunities, build business cases, define acceptance criteria, prioritize roadmaps, and deliver platform improvements. Breaks epics into features and user stories, validates solutions through data and research, manages risk, and communicates product strategy to stakeholders in an agile environment.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with data scientists, analysts, product managers, and software engineers while applying distributed data, lakehouse, streaming, database, and security technologies. Establish reusable engineering patterns, ensure performance and resilience, communicate technical outcomes, mentor team members, and deliver secure data solutions with minimal supervision.

Lead applied AI and data science work for recommendation and personalization systems. Partner with data scientists, engineers, and product managers to deliver customer-facing products; develop, train, evaluate, validate, and implement machine learning models; analyze large-scale numeric and textual datasets; apply statistical modeling, deep learning, causal inference, reinforcement learning, and recommender-system techniques; and communicate technical findings to business stakeholders while developing team talent.

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.