Top Tech Jobs & Startup Jobs in NYC, NY

Own the enterprise Application Security product roadmap, evaluate SAST/DAST/SCA tools, develop AI application-security strategies, partner with engineering teams, govern vulnerability disposition, and communicate cybersecurity risks to executives. The role requires extensive cybersecurity, AppSec, product management, metrics, risk governance, cloud-native, vulnerability management, and people leadership experience.

Design, build, and support scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data security, observability, orchestration, and real-time pipelines. It also requires influencing cross-functional teams, mentoring data professionals, establishing engineering standards, and communicating technical outcomes to stakeholders.

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.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Design secure, resilient, and reusable data engineering solutions; support distributed and streaming workloads; enforce engineering standards; collaborate with product, software, analytics, and data science teams; communicate technical concepts; and mentor other engineers.

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.

Lead engineering projects and developers building cloud-based, AI-powered risk management solutions. Design distributed microservices and full-stack systems, establish architecture and coding standards, mentor engineers, improve performance, reduce technical debt, and guide Agile teams. The role uses multiple programming languages, databases, AWS and other cloud services, Docker, Kubernetes, APIs, CI/CD, infrastructure as code, automated testing, and observability while influencing long-term technology strategy.

Build and architect scalable cloud data solutions, pipelines, platforms, and data products. Lead large-scale data initiatives, make architecture and platform decisions, ensure performance and operational efficiency, and apply modern technologies such as Python, Spark, Databricks, and Snowflake. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical concepts to stakeholders.

Leads international regulatory risk operations for a global payments network. Responsibilities include managing regulatory reporting, audits, examinations, change management, market entry and exit activities, regulatory engagement models, controls, cross-functional alignment, and translating regulatory requirements into scalable business and technology solutions. The role partners with business, product, and technology teams to support compliant global expansion and improve risk management capabilities.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.