My specialization

What i do

Deep Learning

CNN (DenseNet, ResNet, Inception), FCN, RNN (GRU, LSTM, bi-LSTM, stacked-LSTM, VAEs, Object Detection, Transformers, Transfer Learning, customizing, debugging and tuning Neural Networks.

Machine Learning

Linear Regression, logistic Regression, KNN, Naive Bayes, SVM, RL, Bagging & Boosting (ensemble), PCA, SVD,regularization, accuracy measures, statistical analysis and modeling, and knowledge of end-to-end machine learning pipeline (feature engineering, data cleaning, data visualization, model deployment and serving)

Software Development

Data structures and Algorithm, C++, Python, Java, GO Rust, Swift, Pytorch, Tensorflow, FastAI, Scikit-Learn, OpenCV, CUDA, HTML, CSS, JS.
Scrum, Agile process, UML, Build Pipeline, Junit-test, unit test, Java Docs, Doxygen, Build tools (Cmake, gcc, Make, gmake).
Full stack development: RestAPI, GRPC, Flask, NodeJS.
Mobile Development: Android (Java, Kotline), Flutter (Dart), Xcode (Swift), Figma, Adobe XD, Human Computer Intreaction Certified.

Cloud Computing/Deployment

AWS Certified, Kubernetes cretified, Google Cloud platform, Azure.

Experience & skills


July 2020 – Present
Senior Machine Learning Software Engineer


  • Created Alectio’s infrastructure on Kubernetes with OpenShift, Kops, Terraform, Nginx-Ingress, Kubeflow, AWS Cloudfront, AWS VPC, Cloud Trail, and Airflow.
  • Re-designed and implemented Alectio’s backend in Golang and migrated Dynamodb database to Cassandra. Created Dev environment setup using VSCode dev container, used goSwagger to manage and automate lots of code management.
  • Redesigned Alectio’s Client python SDK to work with GraphQL and use GRPC for an end to end communication with Alectio’s server
  • Build hosted Auto Active learning stack with ephemeral compute instances using Kubernetes. Handled provisioning, deletion, data retrieval, deployment monitoring for the training instance by writing infrastructure as code backend to interact with various infrastructure resources.
  • Built Auto labeling stack and deployed using KubeFlow (Tensorflow extended)
  • Built the ETL stack for run analysis on high volume streaming data using Spark and Hadoop
  • Built stack for internal instrumentation using Grafana, Python Flask, and Postgres SQL
  • Built CI/CD pipeline using Jenkins and Gitlab to automate testing of APIs, log changes to Confluence and Receive errors messages

Aug 2019 – July 2020
Machine Learning

Third Insight

From developing deep learning based various components of visual scene understanding deep learning pipeline to building C++ and CUDA based systems, I had a phenomenal experience of wearing a lots of hats and taking ownership.

May 2019 – Aug 2019
ai specialist

ThermoFisher Scientific

Implemented and trained CNN based models for next-generation QPCR and PCR analysis software using Python and Tensorflow. Improved existing system accuracy from 78% to 98%. Solved multi dye cross-talk problems. Developed various methodologies to evaluate the performance of the model according to the domain. Built front-end system to use the Genotyper

Aug 2018 – Jan 2019
Data Science Intern


During the 3 months internship, got the pleasure of building a computer vision application to detect defects in the railway tracks and developing various data analysis algorithms.

Sep 2013 – Oct 2016


In order to help the community with its need for reliable storage, we built a MVP and launched it to aid people in finding a place to store their belongings safely and conveniently

AI (Deep Learning: CNN, LSTM, Transformers, ML: Regression, SVM, SVD, Kmeans Navie Bayes, PCA. End-to-end pipeline)
Web development
Machine Learning
C++, Python, Java
Deployment(Cloud: GCP, AWS Certified, Kubernetes Certified, Build pipelines, docker, AB testing)
Design Patterns

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lines of code


Research Paper


projects done