Top Tech Jobs & Startup Jobs in NYC, NY

Develop, train, and deploy machine learning and generative AI models in production. Focus on engineering aspects: scalable software, MLOps/DevOps integration, CI/CD pipelines, monitoring (e.g., New Relic), and automation frameworks to ensure model reliability and performance.

Participate in projects to improve and standardize technology operations. Collaborate with cross-functional teams to redesign processes, document improvements, create dashboards and reports, analyze data trends, and identify automation opportunities to increase operational effectiveness and adoption of shared tools and practices.

Senior sales leader responsible for driving diversified growth across life, annuity, LTC, disability, and worksite products. Sets sales strategy, leads and develops front-line and sales leadership, partners with enterprise functions, enforces performance and governance, and represents the field to Home Office to deliver profitable, measurable results.

Develop and maintain cybersecurity solutions; monitor and analyze security events; investigate potential threats; strengthen controls across cloud and on‑prem environments; use security tools, automation, and threat intelligence; support secure code/configuration changes and vulnerability remediation; collaborate with engineering and infrastructure teams and leverage AI-powered technologies for detection and response.

Lead design and implementation of the enterprise AI platform, defining reference architectures, reusable patterns, and complex services (lifecycle registries, control plane, memory, model abstraction). Build agentic AI capabilities, ensure secure governed deployments, drive portability and engineering standards, review designs, mentor senior engineers, and deliver production-grade AI systems across cloud-native infrastructure.

Lead and build a small senior AI engineering team to design, build, and operate a governed enterprise AI platform and agentic AI solutions. Define architecture, roadmap, and governance; lead cloud and runtime architecture on Google Cloud; mentor engineers; partner with Security, Risk, and business stakeholders; implement AI FinOps, evaluation, and responsible AI controls; and deliver production-grade generative AI, retrieval, memory, orchestration, and agent lifecycle capabilities.

Design, develop, and enhance secure, scalable software applications using modern programming languages and cloud-native technologies. Collaborate with product, design, and engineering across the SDLC, apply Agile and DevOps practices, and leverage automation and AI-driven tools to improve productivity and code quality.

Build, automate, and support cloud infrastructure for enterprise applications. Develop infrastructure-as-code solutions, standardized platform patterns, and self-service capabilities. Use AWS, containerization, observability, and automation tools to improve reliability, scalability, deployments, and developer experience while collaborating with engineering and platform teams.

Summer internship focused on building scalable data architectures and pipelines, automating data processes, improving data quality frameworks, and supporting cloud infrastructure. Work includes AWS automation, data warehousing, workflow optimization, platform integration, and operationalizing tools to process structured and unstructured data while leveraging analytics, AI/ML, and standardized pipelines.

Design, configure, and operate data governance tooling for unstructured and structured data. Implement metadata cataloging, lineage harvesting, automated data quality checks, and governance workflows. Build prototypes, dashboards, and connectors using Python, SQL, Databricks, and Streamlit. Collaborate with stakeholders to define critical data elements, prioritize remediation, produce documentation, and drive tool adoption via trainings and enablement.