Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I'm Sruthisri Venkateswaran, an Applied Machine Learning Engineer focused on building reliable, low-latency ML systems for real-world problems. I enjoy turning messy data into actionable insights and collaborating with cross-functional teams to deploy end-to-end ML solutions in cloud environments. Currently, I contribute to research at the University at Buffalo while applying industry experience in computer vision, NLP, and data pipelines. I thrive on shipping practical ML features—from data ingestion and model training to inference optimization and monitoring—in production.…I'm Sruthisri Venkateswaran, an Applied Machine Learning Engineer focused on building reliable, low-latency ML systems for real-world problems. I enjoy turning messy data into actionable insights and collaborating with cross-functional teams to deploy end-to-end ML solutions in cloud environments. Currently, I contribute to research at the University at Buffalo while applying industry experience in computer vision, NLP, and data pipelines. I thrive on shipping practical ML features—from data ingestion and model training to inference optimization and monitoring—in production.

Sruthisri Venkateswaran

Data Scientist, AI Engineer, AI Developer, +2





I'm Sruthisri Venkateswaran, an Applied Machine Learning Engineer focused on building reliable, low-latency ML systems for real-world problems. I enjoy turning messy data into actionable insights and collaborating with cross-functional teams to deploy end-to-end ML solutions in cloud environments. Currently, I contribute to research at the University at Buffalo while applying industry experience in computer vision, NLP, and data pipelines. I thrive on shipping practical ML features—from data ingestion and model training to inference optimization and monitoring—in production.…I'm Sruthisri Venkateswaran, an Applied Machine Learning Engineer focused on building reliable, low-latency ML systems for real-world problems. I enjoy turning messy data into actionable insights and collaborating with cross-functional teams to deploy end-to-end ML solutions in cloud environments. Currently, I contribute to research at the University at Buffalo while applying industry experience in computer vision, NLP, and data pipelines. I thrive on shipping practical ML features—from data ingestion and model training to inference optimization and monitoring—in production.

Available to hire

I’m Sruthisri Venkateswaran, an Applied Machine Learning Engineer focused on building reliable, low-latency ML systems for real-world problems. I enjoy turning messy data into actionable insights and collaborating with cross-functional teams to deploy end-to-end ML solutions in cloud environments.

Currently, I contribute to research at the University at Buffalo while applying industry experience in computer vision, NLP, and data pipelines. I thrive on shipping practical ML features—from data ingestion and model training to inference optimization and monitoring—in production.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

Research Assistant at University at Buffalo (LLM Systems)

November 1, 2025 - Present

Built data ingestion and preprocessing pipelines for an LLM-powered academic assistant, transforming unstructured university web pages and policy documents into structured, query-ready datasets. Designed modular indexing pipelines to support retrieval-augmented generation (RAG) with improved grounding and reduced hallucinations.

Research Assistant at University at Buffalo (Computer Vision)

February 1, 2025 - October 1, 2025

Implemented end-to-end computer vision pipelines for smart parking detection, including frame preprocessing, region-of-interest extraction, model inference, and post-processing logic for occupancy estimation from continuous video streams. Trained and tuned YOLO-based object detection models on 10K+ annotated images, iterating on batch size, input resolution, and GPU utilization to reduce inference latency by 20% and training time by 35%. Built a real-time inference and analytics pipeline supporting sustained 60 FPS video streams, producing live heatmaps and occupancy metrics consumed by downstream visualization dashboards in city-scale deployment scenarios.

Software Developer Student Assistant at University at Buffalo

July 1, 2025 - September 1, 2025

Refactored a 5K+ LOC legacy scientific simulation codebase into a modular Python architecture, separating computation, I/O, and configuration layers to improve maintainability and reduce runtime by 30%.

Full Stack Development Intern at Qu Anytime

January 1, 2025 - May 1, 2025

Integrated ML-backed backend services with AWS-hosted REST APIs and CI/CD workflows, enabling reliable low-latency application features for 500+ concurrent users.

Assistant Manager at Jio Platforms Limited

December 1, 2023 - July 1, 2024

Developed and maintained large-scale ML data pipelines using Python, SQL, and PySpark across Databricks and Snowflake, handling high-volume transactional and behavioral data with a 50% reduction in end-to-end ETL latency. Implemented recommendation and NLP-based analytics workflows that processed live user interaction data, generating features and insights that drove a 25% increase in engagement across targeted product surfaces.