Engineered flagship customer-facing features, directly driving enterprise contract acquisitions, high-stakes sales demos, and fundraising

Owned the company-wide Observability strategy from inception to adoption, driving vendor negotiations, Grafana architecture, and long-term telemetry standards

Spearheaded the Data Gateway development and integration, establishing an architectural cornerstone that abstracts data access to drive system security and developer ergonomics

Acted as the de facto DevOps lead, bridging backend architecture and cloud provisioning to build resilient, scalable systems

Executed cross-functionally across frontend, backend, and infrastructure, delivering end-to-end solutions to support internal operations and high-growth product capabilities

Managed and contributed to infrastructure improvements, enhancing reliability, scalability, performance, and observability across compute, storage, security, network layers for engineering team, utilizing AWS, Terraform, Kubernetes, and DataDog

Co-Led org-wide API Gateway adoption, coordinated with microservice owners to drive migration, improving security

Enhanced observability and issue monitoring, achieving $48K/yr savings in telemetry ingestion, 10% faster database performance through query optimization, and improved pytest runtime via parallelization

Proposed and led CI/CD pipelines and runners overhaul, reducing wait time by 75% (~6 dev-hours/day) and saving $12K/yr

Automated backup and implemented disaster recovery for Vault and RDS (PostgreSQL) using Airflow, EKS, and ArgoCD

Led end-to-end integration of large language models that powers core AI functionalities encompassing backend development, ML infrastructure, MLOps(model fine-tuning, serving/deployment, evaluation, and data wrangling & pipeline)

Developed core backend components in Go, including ML service integration, A/B testing, content-based recommendation system, improving engagement and data-driven optimizations utilizing Postgres, ClickHouse, Elasticsearch

Architected and maintained distributed GPU machine learning infrastructure using Kubernetes and Grafana, scaling to handle 1 million daily requests with 98% uptime while ensuring high availability, fault tolerance, and observability

Cut infrastructure costs by $400K/yr through LLM inference optimization using concurrency tuning, batching, quantization

Built data pipeline for extensive data wrangling of 15M+ data points and fine-tuned Large Language Models with curated data, increasing user satisfaction by 25% with Pandas, WandB, Axolotl

Drove cross-functional product efforts in full-stack development using React Native, Express, and PostgreSQL, resulting in 10% increase in 7-day user retention and 50% longer session duration for 30K+ customers

Collaborated with marketing team to develop and integrate granular tracking of user engagement metrics, funnel analysis, and revenue attribution, driving 50% improvements in user acquisition and enabling data-driven decision-making

Architected and implemented ETL pipelines using Redis and PostgreSQL yielding 7% annual service costs savings

Led development of natural language-driven data analysis and management dashboards using React.js, Express, and PostgreSQL, resulting in successful investor demos and early access to GPT models

Implemented smart spreadsheet linked to databases and external platforms that enables better data management

Built a self-serve onboarding system for new customers allowing for integration of multiple data platforms