Top Tech Jobs & Startup Jobs in NYC, NY

Owns backend product features powering digital airport lounge experiences. Translates business epics into features and user stories, prioritizes roadmaps, defines acceptance criteria, validates solutions through data and research, and collaborates with technology, design, data science, strategy, marketing, and guest experience teams in an agile environment.

Design, build, deploy, and support enterprise AI systems including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for scalability, cost, latency, throughput, and hardware utilization. Establish enterprise AI architecture standards, lead multi-cloud strategies, mentor engineering teams, promote responsible AI, and influence long-term technical roadmaps.

Owns A/B testing and optimization of credit card landing-page experiences. Partners with marketing to align performance strategy and conversion goals, while leading designers, engineers, business analysts, and legal partners in delivering personalized digital journeys. Applies customer-centered, business-focused, technology-driven, and transformational product management practices to create innovative consumer-facing products.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and observability. Lead multi-model orchestration, cost-performance governance, technical design reviews, and AI platform roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while mentoring senior technical teams and promoting responsible AI standards.

Build and lead full-stack engineering solutions using Scala, React, AWS, microservices, and cloud technologies. Lead technical projects and developers, define service architectures, improve system performance, reduce technical debt, establish engineering standards, mentor engineers, and drive cross-team initiatives. Develop scalable APIs and distributed systems while contributing to cloud infrastructure, CI/CD, automated testing, observability, and long-term technology strategy.

Lead full-stack engineering initiatives and developer teams building cloud-based, distributed microservices and full-stack systems. Define service boundaries, integration patterns, coding standards, and reusable frameworks; mentor engineers; collaborate with product managers and architects; and improve performance, reliability, and technical debt. The role uses modern programming languages, AWS and other cloud technologies, databases, containers, CI/CD, automated testing, observability, and AI-assisted development tools.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with data scientists, analysts, engineers, and product managers while applying distributed computing, lakehouse architecture, data modeling, security, and observability practices. Develop resilient relational and NoSQL data solutions, mentor team members, enforce engineering standards, and communicate technical outcomes to stakeholders. The role requires independent delivery of cloud-first data products supporting large-scale financial experiences.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish performance governance, lead technical design reviews, shape long-term platform strategy, and mentor senior AI engineering leaders.

Lead technology projects and developers building cloud-based, full-stack risk management solutions. Design distributed microservices, APIs, reusable frameworks, and database-backed systems while partnering with product managers and architects. Provide technical leadership across Agile teams, establish engineering standards, mentor engineers, improve system performance, reduce technical debt, and evaluate emerging technologies including AI tooling. The role requires hands-on software engineering across modern programming languages, cloud platforms, container orchestration, CI/CD, automated testing, and observability.

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, designing data pipelines, operating distributed systems and cloud platforms, monitoring and retraining models, automating testing and deployment, and applying responsible AI practices. The role collaborates with Product and Data Science teams to deliver scalable solutions using frameworks such as PyTorch or TensorFlow and platforms including AWS, Kubernetes, Spark, and Ray.