Top Tech Jobs & Startup Jobs in NYC, NY

Own strategic growth, revenue, and profitability for major P&C insurance accounts. Build C-suite relationships, develop account plans, identify AI/data/cloud opportunities, lead commercial negotiations, and coordinate global delivery to drive client transformation and expansion. Travel up to 50%.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

The AI Solutions Lead will expand Quantiphi's business in key insurance accounts by managing executive relationships, developing account strategies, leading sales initiatives, and ensuring client satisfaction.

Design, develop, and deploy production forecasting models and agentic AI solutions on GCP. Perform EDA, feature engineering, experiment design, and model interpretability. Build and integrate AI agents (LangChain, Vertex AI Agents) into quotation and forecasting workflows, maintain MLOps pipelines (Vertex AI Pipelines, Cloud Composer), and present results to stakeholders.

Lead per-SYSID assessments and design L2 sequence and data-flow diagrams. Define Kafka topic topology and microservice boundaries, plan Oracle AQ to Confluent Kafka migrations, assess Spring Boot migration paths, design AWS Step Functions for batch orchestration, and produce current vs. future-state architectures.

Architect and implement MLOps strategies, design ML pipelines, mentor teams, and ensure compliance on Google Cloud, focusing on AI/ML best practices.

Design and lead large-scale AWS migrations (rehost/replatform), architect cloud infrastructure and platforms, automate migrations with IaC and scripting, troubleshoot OS/database issues, and implement hybrid networking and security best practices.

Design and deliver enterprise-grade Generative AI solutions on AWS using Bedrock and AgentCore. Architect LLM-based applications, RAG pipelines, agentic workflows, vector DB integrations, and APIs. Optimize models for performance, cost, and quality, ensure security and governance, lead teams, and troubleshoot production GenAI systems.

Architect and implement enterprise-grade MLOps/LLMOps platforms on AWS and Kubernetes. Design ML/LLM pipelines for training, validation, deployment, monitoring, governance, CI/CD, and observability. Serve as technical authority, conduct architecture reviews, ensure security and compliance, and mentor engineering teams across projects.

Lead end-to-end delivery of enterprise Generative AI programs (LLMs, RAG, Agentic AI). Define roadmaps, manage multi-workstream global teams, align technical delivery with business outcomes, guide architecture, MLOps, GPU/inference optimization, vector DB and fine-tuning strategies, ensure AI governance, and maintain executive stakeholder relationships.