I had a great time talking with Rahil and learning from his experience. His insight into graduate programs in Canada is invaluable to anyone looking to pursue higher studies. He's very friendly and helpful as well!
Are you the shy kid? No worries shoot your shot here
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About me
TL;DR ➡️ Machine Learning Engineer × Data Scientist × Software Engineer
➡️ 3+ years of experience across Machine Learning, Software Engineering, and Data Science
I currently work on enterprise-scale Generative AI systems at ALS, contributing to the development and scaling of a global GenAI platform with multi-agent and multimodal capabilities. My focus includes production architecture, AI governance, and cost-efficient large-scale deployment.
At ALS Geoanlytics, I worked in Mineral Exploration and Geoscience, applied Deep Learning and Image Processing to complex geological datasets and translated AI research into operational impact.
Previously at Statistics Canada, I developed a Crop Classification and Regression Deep Learning model that accurately predicted crop content and acreage across three Canadian provinces, strengthening my expertise in remote sensing and geospatial analytics.
Before my master’s, I worked as a Backend Software Engineer using Spring Boot and AWS, building scalable APIs and distributed systems. I also served as a Deep Learning Research Intern across research groups in India, further shaping my passion for applied AI.
Outside work, I actively implement research papers in ML/DL, solve algorithmic problems, and optimize SQL queries on LeetCode. I’m also an avid bird-watcher, hiker, and enjoy cricket, soccer, and tennis.
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Technical Proficiencies
• Generative AI (LLMs, Agents, RAG, Multimodal Systems)
• Deep Learning (CV/NLP)
• Machine Learning
• Cloud-Native Architecture & DevOps
• AI Governance & Cost Optimization
• Remote Sensing & Geospatial Analytics
• Data Engineering (Databricks, PySpark)
• Backend & API Development
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Technical Skills
Languages: Python, R, Java, SQL, MATLAB, C++, JavaScript
GenAI & AI: Vercel AI SDK, LLM integrations, multi-agent workflows, RAG pipelines, TTS/STT
ML/DL Libraries: PyTorch, TensorFlow, Keras, scikit-learn, Pandas, NumPy, OpenCV, NLTK
Data & Big Data: Databricks, PySpark, Hadoop, Kafka, Hive, Tableau
Cloud & DevOps: Azure, AWS, Docker, Kubernetes, Kubeflow, CI/CD
Databases: MySQL, PostgreSQL, MongoDB, Azure CosmosDB (Mongo API), NeptuneDB
SWE Stack: Spring Boot, REST, GraphQL, ReactJS, Next.js, FastAPI, Git, Jira
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📩 Feel free to connect here or reach out at rahilbalar98@gmail.com for exciting opportunities.