rohit krishnan
@rohitkrishnan
Machine Learning Engineer with expertise in AI-driven solutions.
What I'm looking for
I am a dedicated Machine Learning Engineer with a strong background in developing innovative AI solutions. My experience includes fine-tuning GPT-3 for enhanced chatbot reasoning and architecting vision pipelines for real-time posture tracking. At AmiableAi Inc., I successfully boosted query accuracy by 40% across numerous AWS services, demonstrating my ability to deliver impactful results.
My journey in machine learning has also involved creating a RAG-based legal QA system that significantly reduced analysis time and drafting errors. I have a proven track record of optimizing machine learning pipelines, as seen in my work with VisionBox Inc., where I migrated ML and data pipelines to AWS, increasing throughput by 65%. My academic background, including a Master of Science in Computer Science, has equipped me with the skills necessary to tackle complex challenges in the field.
Experience
Work history, roles, and key accomplishments
Machine Learning Engineer
AmiableAi Inc.
Jan 2023 - Oct 2023 (9 months)
Fine-tuned GPT-3 to generate structured GraphQL queries, boosting Cloud X-Ray's multi-step query accuracy by 40%. Architected a vision pipeline using YOLOv8 for real-time posture tracking at 60 FPS, reducing inference cost by 3x. Engineered a RAG-based legal QA system using LangChain and Pinecone, cutting analysis time by 72%.
Machine Learning Engineer
VisionBox Inc.
Jan 2022 - Jan 2023 (1 year)
Migrated ML and data pipelines to AWS using SageMaker, API Gateway, and DocumentDB, boosting throughput by 65%. Designed a scalable sleep prediction pipeline with clustering, shrinking deployment scale from 6,000 to 60. Led drone-based vision data strategy for crop monitoring and trained models for plant disease detection, projecting a 17% yield improvement.
Research Intern
Medical Image Analysis Lab
May 2020 - Aug 2020 (3 months)
Implemented a ResNet-152-based classification pipeline on chest X-rays for COVID vs. pneumonia classification. Integrated Grad-CAM and saliency maps, achieving 93.2% predictive accuracy with strong interpretability.
Associate Software Engineer
Prodapt Solutions
May 2018 - Jul 2019 (1 year 2 months)
Developed an XGBoost model for defect classification, significantly improving defect reporting efficiency. Automated telecom analytics pipelines across engineering workflows, saving over 30 manual analysis hours per month.
Education
Degrees, certifications, and relevant coursework
Simon Fraser University
Master of Science, Computer Science (Visual Computing)
Sri Sairam Engineering College
Bachelor of Engineering, Computer Science
Availability
Location
Authorized to work in
Job categories
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