personal_data/experience.md

Professional Experience

Software Engineer - AI Platform | Cloud Systems LLC | US Remote

Jul 2025 - Present

• Agentic Workflow Automation: Architected an agentic workflow using LangGraph and ReAct to automate SQL generation. This system automated ~65% of ad-hoc data-auditing requests from internal stakeholders, reducing the average response time from 4 hours to under 2 minutes.
• ETL Optimization: Optimized data ingestion performance by rebuilding ETL pipelines with batched I/O, incremental refresh logic, and dependency pruning, cutting daily execution runtime by 25%.
• Infrastructure & Reliability: Improved production reliability by shipping the agent service on Kubernetes with autoscaling and rolling deploys, adding alerts and rollback steps for failed releases.
• Contract Testing: Improved cross-service reliability by implementing Pydantic schema validation and contract tests, preventing multiple breaking changes from reaching production.

Machine Learning Engineer | Virginia Tech, Dept. of Plant Sciences | Blacksburg, VA

Aug 2024 - Jul 2025

• Model Optimization: Increased genomics sequence classification throughput by 32% by applying LoRA and soft prompting methods. Packaged repeatable PyTorch pipelines that cut per-experiment training time by 4.5 hours.
• HPC Orchestration: Developed an ML orchestration layer for distributed GPU training on HPC clusters. Engineered checkpoint-resume logic that handled preemptive node shutdowns, optimizing resource utilization and reducing compute waste by 15%.
• MLOps: Reduced research environment setup time from hours to minutes by containerizing fine-tuned models with Docker and managing the experimental lifecycle (versions, hyperparameters, and weights) via MLflow.

Software Engineer | UJR Technologies Pvt Ltd | Hyderabad, India

Jul 2021 - Dec 2022

• API & SDK Development: Designed and maintained standardized REST APIs and Python-based SDKs to streamline the ML development lifecycle, reducing cross-team integration defects by 40%.
• Model Serving: Engineered model-serving endpoints with automated input validation and deployment health checks, lowering prediction-related failures by 30% for ML-driven features.
• CI/CD Pipeline: Automated CI/CD pipelines via GitHub Actions with comprehensive test coverage and scripted rollback procedures, decreasing release failures by 20% across production environments.