Lead a hands-on platform engineering function spanning full-stack product development, real-time Kafka and ClickHouse data/ML pipelines, cloud infrastructure, DevOps, security, and reliability. Own React, Node.js, and MongoDB applications; Terraform, Kubernetes, AWS/GCP, Docker, and GitHub Actions; production observability and incident response; and SOC 2 readiness. Establish AI-assisted engineering workflows using tools such as Claude Code and Cursor while replacing vendor-built infrastructure with durable internal automation.

Lead end-to-end development of scalable web applications using Node.js, React, MongoDB, and Google Cloud. Build analytics, identity resolution, personalization, integrations, and advanced visualizations using AI-assisted, spec-driven workflows. Develop LLM integrations, configure AI agents, maintain MCP servers, automate testing and CI/CD, and support observability. Serve as a Feature Lead by guiding architecture, technical specifications, documentation, and cross-functional communication.

Designs, deploys, and operates production machine learning systems for identity resolution, scoring, deliverability, and personalization. Responsibilities include feature engineering, model training, real-time inference, graph database integration, monitoring, drift detection, retraining, low-latency APIs, and scalable data processing. The role also contributes to MLOps standards, GPU acceleration, and ML architecture while partnering with platform engineering.