Koushik Sivarama Krishnan

Machine Learning Engineer | NLP, Computer Vision & Speech | GenAI, RAG & RL Agents

Biography

I'm a Machine Learning Engineer who loves turning messy text, images, and audio into reliable AI products. I recently completed my MEng in Applied Data Science at the University of Victoria, and over the last few years I've focused on building GenAI, LLM/RAG systems, and RL-powered agents that help real teams search faster, decide better, and trust their tools.

Education

Master of Engineering - Applied Data Science, Sept 2023 - August 2025

University of Victoria

Bachelor of Engineering - Computer Science, Aug 2019 - April 2023

Panimalar Engineering College

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Experience


Co-op Data Scientist, Insurance Corporation of British Columbia (ICBC)

September 2024 - Present (full-time)

  • Developed end-to-end implementation of ICBC's in-house agentic AI assistant using Retrieval Augmented Generation (RAG) with LangChain, Milvus, and MCP servers, working closely with senior data scientists to design architecture and validate performance improvements.
  • Applied causal inference techniques to evaluate the Comprehensive Medical Assessment (CMA) program, guiding stakeholders with data-driven decisions.
  • Designed a recommendation engine to prioritize insurance claims, blending business logic and data-driven methods to boost decision support and efficiency.

Research Engine Developer, Justice Data and Design Lab

May 2024 - February 2025 (part-time)

  • Collaborated with MITACS to leverage data analysis and machine learning, aiming to enhance access to justice through evidence-based opportunities.
  • Doubled the performance and efficiency of the research engine chatbot using Retrieval Augmented Generation (RAG) to better identify and address unmet legal needs.
  • Conducted advanced data analysis on large-scale legal text datasets from Reddit and People's Law School using Topic Modeling and Power BI visualizations.

Machine Learning Engineer, SeiSei.ai

April 2023 - July 2023 (full-time)

  • Fine-tuned the FreeVC voice conversion model, achieving a 15% performance boost on Hindi audio through hyperparameter tuning and preprocessing.
  • Directed and supervised a team of interns to deploy ML models with Truefoundry, accelerating project delivery and performance targets.
  • Enhanced data delivery and automated data pipelines for 5000+ hours of audio, improving system performance.

Computer Vision Intern, Drive Analytics

September 2021 - February 2022 (part-time)

  • Engineered a Major League Baseball video analytics system deployed on AWS using Celery, RabbitMQ, and Django.
  • Led a team in image-to-3D model rendering, increasing profits by 10% through innovative vision techniques.
  • Conducted comparative analysis of object detection models for glove and baseball detection.

Deep Learning Intern, MURF.ai

December 2020 - June 2021 (part-time)

  • Implemented a voice cloning application using state-of-the-art deep learning algorithms.
  • Improved an existing speech-to-text model for better filler word detection.
  • Created a comprehensive Docker script to automate pipelines and deployed it using AWS SQS and Amazon ECS for scalable performance.

Projects



virtual-eye: drowning detection system

Virtualeye - Life Guard For Swimming Pools To Detect Active Drowning

An active drowning detection system built using YOLOv5 constantly monitors the swimmers using the underwater camera feed and triggers an alarm when a person the system detects a drowning person. This system is highly accurate and works in real-time on low compute devices.

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Text To SQL

A Transformer model trained on WikiSQL dataset that accepts natural language as input and returns SQL Query as output. This model is deployed using streamlit.

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Image Captioning 📸 ⇒ 📝

An encoder-decoder based model to caption images built using PyTorch and deployed using Streamlit. This model uses inceptionV3 as encoder and LSTM layers as decoder. This model is trained on Flickr30k dataset.

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Brain MRI FLAIR Segmentation

A Complete MLOPS project on Brain MRI FLAIR Segmentation. A MobileNet v3 based segmentation project to perform instance segmentation on FLAIR (Fluid-Attenuated Inversion Recovery) abnormality in brain MRI images. This model is trained using Brain MRI segmentation from kaggle and is deployed on Heroku.

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Skills

Machine Learning & Deep Learning
90%

90%

NLP, LLMs & RAG
90%

90%

Computer Vision & Speech
85%

85%

MLOps & Backend APIs (FastAPI, Flask)
85%

85%

Data Analysis, Experimentation & Evaluation
80%

80%

Cloud & DevOps (AWS, Azure, GCP, Docker, Kubernetes)
80%

80%

Python
95%

95%

SQL & Databases (PostgreSQL, MySQL, MongoDB)
80%

80%

Git & Collaboration
80%

80%

Visualization & BI (Power BI, Dashboards)
75%

75%

Publications


Vision Transformer based COVID-19 Detection using Chest X-rays

Koushik Sivarama Krishnan, Karthik Sivarama Krishnan

ArXiv IEEE


SwiftSRGAN - Rethinking Super-Resolution for Efficient and Real-time Inference

Koushik Sivarama Krishnan, Karthik Sivarama Krishnan

ArXiv IEEE


Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Dataset

Koushik Sivarama Krishnan, Karthik Sivarama Krishnan

ArXiv IEEE

Get in Touch