Top Tech Jobs & Startup Jobs in NYC, NY

Build, deploy, scale, and operate production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, managing cloud-based distributed systems, automating testing and deployment, monitoring production models, and applying responsible AI practices. The role collaborates with Product and Data Science teams in an Agile environment and uses technologies such as Python, PyTorch, Spark, Kubernetes, and cloud platforms.

Leads multiple technology projects and engineering teams developing distributed microservices, full-stack systems, and cloud-native platforms. Provides technical leadership on architecture, system design, reliability, performance, platform roadmaps, and shared services. Partners with product managers and architects, mentors engineers, drives technology strategy, establishes coding standards, and delivers secure solutions supporting regulatory and compliance needs.

Leads quantitative analysis for commercial and counterparty credit risk modeling. Responsibilities include developing, validating, and improving statistical, econometric, financial, and machine learning models; assessing model risk and governance; analyzing large datasets; automating processes; documenting methodologies; presenting results to nontechnical stakeholders; and managing complex cross-team projects. The role may also involve people management and requires strong expertise in financial risk exposures, regression, time-series, survival, panel, and machine learning methods.

Design, develop, optimize, deploy, and support foundational AI systems, including LLMs, agents, vector search, model evaluation, orchestration pipelines, and responsible AI controls. Lead cost-performance governance, technical design reviews, roadmap development, and optimization of model and hardware utilization. Mentor senior AI engineers while partnering with research, product, program, and engineering teams to deliver scalable production AI platforms.

Lead full-stack engineering projects and development teams building cloud-based, distributed microservices and full-stack systems. Provide technical leadership, establish engineering standards, mentor developers, define service architectures, improve system performance, reduce technical debt, and influence long-term technology strategy. The role uses multiple programming languages, databases, AWS services, Docker, Kubernetes, CI/CD, infrastructure as code, automated testing, and modern AI development tools.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, guide hybrid and multi-cloud strategies, and promote responsible AI practices. Lead technical direction, mentor engineering teams, collaborate with cross-functional stakeholders, and advance research techniques into production.

Provides financial analysis, reporting, budgeting, forecasting, P&L management, and strategic insights for the Global Payments Network Tech organization. Develops financial processes, reporting cadences, models, presentations, and variance analyses to support leadership decision-making. Partners across Finance, Technology, and business teams, evaluates platform convergence impacts, communicates findings to senior leaders, and manages ad hoc projects and priorities independently.

Leads full-stack engineering projects and developer teams building cloud-based, distributed microservices and customer-facing systems. Responsibilities include defining service boundaries, developing APIs and reusable frameworks, applying AWS and modern programming technologies, improving performance, reducing technical debt, setting engineering standards, mentoring engineers, and guiding Agile teams. The role also influences technology strategy, promotes operational excellence, and leverages emerging AI tooling and observability practices.

Design, build, deploy, monitor, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Apply modeling, feature selection, validation, responsible AI, and explainability practices while collaborating with product, data science, and engineering teams in an Agile environment.

Lead engineering projects and developers building cloud-based, AI-powered risk management solutions. Design and deploy full-stack microservices, APIs, and distributed systems using modern programming languages, databases, AWS services, containers, and messaging technologies. Provide technical leadership across Agile teams, establish engineering standards, mentor engineers, partner with architects and product managers, reduce technical debt, improve system performance, and influence long-term technology strategy.