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amol damare

@amoldamare

Senior AI Engineer at Tingono Inc., where I cut inference processing time by 70% across workloads covering over 10,000 customer accounts.

United States

Message

At Tingono Inc., I architected an AWS EMR inference pipeline that reduced processing time by approximately 70% across workloads covering over 10,000 customer accounts. I also shipped a caching layer that cut query and graph load times from 10–15 seconds to under one second.

At Stony Brook University, I co-developed and deployed runtime-assurance systems for AI-driven microgrids, including barrier-certificate-based Simplex architectures for safety under adversarial conditions. My work combines production AI/ML systems with research in formal verification, runtime assurance, and hybrid systems.

Experience

Work history, roles, and key accomplishments

Current

Senior AI Engineer

Current

Tingono Inc.

Jun 2024 - Present (2 years 4 months)

Served as primary technical decision-maker for the AI/ML platform, architected a distributed inference pipeline on AWS EMR, and shipped a caching layer that cut query and graph load times from 10-15s to under 1s. Designed GenAI agents that cut customer-success update work by approximately 80% and improved churn-prediction AUC by approximately 20%.

Amazon EMR Redshift PyTorch GenAI Deep Learning Distributed Systems Jenkins Git Mentorship

Research Assistant

Stony Brook University

Sep 2019 - May 2024 (4 years 8 months)

Collaborated on runtime-verification team to co-develop and deploy AI-driven microgrid systems under NSF's AI Grid project, building barrier-certificate-based Simplex architectures for formal safety guarantees. Extended the framework to hybrid systems and deployed on real-world microgrid controllers, including for ComEd.

Formal Verification Runtime Assurance Neural Operators Airflow Kubernetes Jenkins Gitlab ML Infrastructure

Senior Software Engineer

GlimpzIt Inc.

Mar 2013 - Aug 2017 (4 years 5 months)

Designed and built a production deep-learning pipeline in Python and PyTorch and backend infrastructure supporting up to 10,000 requests per second. Improved customer-facing response times by approximately 30% through caching and reduced backend latency by approximately 20% through workflow optimization.

Python PyTorch Deep Learning Caching Backend Infrastructure Performance Optimization