Krishnan G
Head Of Engineering at DataGenie
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolKrishnan G is a Head of Engineering and data-driven startup leader based in Bengaluru with 7 years of experience building augmented analytics and cloud-optimized products. He has progressed through hands-on roles at DataGenie from data scientist to engineering lead and now heads engineering to scale conversation analytics, anomaly detection, and time-series capabilities. Deeply versed in data science, cloud architecture, and MLOps, he has contributed deployment robustness to the well-known PyCaret project, improving model deployment across AWS, GCP and Azure. His background in theoretical computer science underpins a pragmatic approach to designing cost-efficient, scalable cloud systems tailored for startups. Known for blending product focus with operational rigor, he mentors teams to ship reliable ML-first features while keeping an eye on market trends. Outside work he channels competitive energy into table tennis and draws creative inspiration from swimming and music.
7 years of coding experience
4 years of employment as a software developer
Senior Secondary, CBSE, Computer Science, 93.4%, Senior Secondary, CBSE, Computer Science, 93.4% at Sindhi High SchoolMaster of Science, Theoretical Computer Science, 9.03, Master of Science, Theoretical Computer Science, 9.03 at PSG College of Technology
English, Tamil, Hindi, Kannada, Malayalam
Github Skills (12)
azure10
mlops10
boto10
microsoft-azure10
aws10
python10
gcp10
machine-learning9
google-cloud-storage9
docker4
dockers4
cicd4
Programming languages (6)
Github contributions (5)
pycaret/pycaret
Jun 2021 - Jun 2021
An open-source, low-code machine learning library in Python
Role in this project:
MLOps Engineer
MLOps EngineerContributions:2 reviews, 12 commits, 3 PRs in 1 day
Contributions summary:Krishnan's commits primarily focused on enhancing the deployment capabilities of the PyCaret library. Their contributions involved refactoring the `deploy_model` function by adding validation checks, improving error messages, and handling exceptions related to cloud provider integrations (AWS, GCP, Azure). They addressed library dependencies and updated the documentation. Their work reflects a focus on the operational aspects of machine learning model deployment, ensuring robustness and ease of use.
pythonpycarettime-seriesclassificationdata-science
KrishnanSG/holt-winters
Mar 2020 - Jun 2021
Contributions:28 commits, 5 pushes, 1 comment in 1 year 2 months
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