Sushant Burnawal

Co-Founder

Bengaluru, Karnataka, India7 yrs 2 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in developing scalable AI solutions.
Led innovative projects in data science and machine learning.
Strong background in cloud services and AI technologies.

Stackforce AI infers this person is a Data Science and AI Engineering expert with a focus on SaaS solutions.

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Skills

Core Skills

Ai EngineeringData ScienceMachine Learning

Other Skills

LangChainCustom Vector DatabaseVirtual Try-On SaaS PlatformDeep LearningVision-based AlgorithmsFastAPIKubernetesCI/CDTeam ManagementPythonElasticsearchWeaviateBilateral Segmentation NetworkParser-Free Appearance Flow NetworkMediapipe

About

As a Senior AI Engineer at Skypoint, I enhance AI platforms and copilots for regulated industries, focusing on creating cost-effective, LLM-agnostic solutions. My work includes innovating with vLLM inference engines and multi-query RAG systems to improve efficiency and accuracy in data processing. Previously, as a Senior Associate in Data Science at Publicis Sapient, I led the development of cutting-edge solutions like a custom vector database and a virtual try-on SaaS platform, leveraging deep learning and vision-based algorithms. I hold a Bachelor of Technology (Honors) from the Indian Institute of Technology, Kharagpur, graduating with a GPA of 8.2. My technical expertise spans Python, C++, R, and SQL, with extensive experience in TensorFlow, PyTorch, and cloud services like Azure, AWS, and GCP. I am also skilled in databases such as Elasticsearch and Weaviate. Certified in Azure AI and MLOps, I continue to expand my knowledge across multiple domains, including blockchain technology and optimization algorithms. My goal is to develop impactful, scalable AI solutions, with a passion for exploring new technologies and methodologies in AI and machine learning.

Experience

7 yrs 2 mos

Total Experience

1 yr 11 mos

Average Tenure

1 yr 5 mos

Current Experience

Zepto

AI/Quant

Dec 2024 – Present · 1 yr 5 mos · Bangalore Urban · On-site

Foundry

AI/ML Engineer

Oct 2024 – Nov 2024 · 1 mo · Bengaluru, Karnataka, India · On-site

At Foundry, we‘re not waiting for a decentralised financial future — we‘re building it now. By empowering institutions with the tools they need to mine and stake digital assets, we‘re thoughtfully driving the industry forward. Learn more at foundrydigital.com

Madyam

Co-Founder

Dec 2023 – Present · 2 yrs 5 mos · Bengaluru, Karnataka, India · Remote

Skypoint

Sr. AI Engineer

Dec 2023 – Sep 2024 · 9 mos · Bengaluru, Karnataka, India · Hybrid

LangChainAI Engineering

Almabetter

Data Science Instructor

Jan 2022 – Jun 2023 · 1 yr 5 mos

Publicis sapient

Senior Associate Data Science L1

Aug 2021 – Nov 2023 · 2 yrs 3 mos

Knowledgebase | Custom Vector Database
Led client engagements and created a custom vector database, a python library
with a pluggable interface for Elasticsearch and Weaviate.
Expanded the use-cases including document insertion to create AsktheDocs by
making it compatible with OpenAI embeddings and Langchain library
Served the Vector Database over FastAPI and deployed it on a Kubernetes
cluster while developing CI/CD using Github Actions and ArgoCD
Ensured the robustness and stability of the system by rigourous testing of
python library, FastAPI and API client using pytest to a coverage of 91%
Virtual Try-on | Vision-based Saas Platform
Created a Virtual Try-On based SaaS-platform which has varied offerings
including Clothes, Hairstyle, Haircolor, Makeup and Jewellery Try-On
Used Bilateral Segmentation Network(BiSeNet) for semantic segmentation of
the facial features of humans into 17 different classes
Implemented Parser-Free Appearence Flow Network for on custom clothing
dataset generated using Pose estimation and Human Parser Segmentation
Trained custom CNN models using transfer learning for adding pre-validations
checks and post-processing enchancement
Used Mediapipe and CVzone libraries for the generation of face mesh, facial
landmarks and background removal for real-time image processing
Managed a team of 4 frontend and DevOps engineers to design and develop a
feedback system for the SaaS-platform for consumer analytics
Developed CI/CD for the SaaS-platfrom in AWS using Serverless Function, API
Gateway for 100k+ users with a max limit of 1000 concurrent users

Custom Vector DatabaseVirtual Try-On SaaS PlatformDeep LearningVision-based AlgorithmsFastAPIKubernetes+4

Awaaz, iit kharagpur

3 roles

Editor

Jul 2020 – Apr 2021 · 9 mos

Sub-Editor

Jul 2019 – Jul 2020 · 1 yr

Senior Reporter

Jul 2018 – Jul 2019 · 1 yr

Effat university

Research Intern

Jul 2020 – Aug 2020 · 1 mo · Jeddah, Makkah, Saudi Arabia

Implemented Ensemble Learning on 16 different models to determine the correlation between the emotions during games and neuro disorders
Performed Feature Extraction using higher-order statistics on EMG signal using wavelet-based methods like DWT, TQWT, WPD and DT-CWT
Compared Kappa Statistic and ROC curves of results and obtained a combined classification accuracy of 99.25% post 10-fold cross validation

Publicis sapient

Associate Data Science

May 2020 – Jun 2020 · 1 mo · Bengaluru, Karnataka, India

Created a Multi-objective approach to solve the Vehicle Routing Problem for Route Optimisation with constraints of Time window
Implemented Metaheuristic approaches like Genetic Algorithms, Ant Colony Optimisation, Particle Swarm and Iterative Local Search
Implemented modified Asynchronous Advantage A-C with input tuples containing static and dynamic elements to cater the varying demands
Used LSTM-Decoders coupled with Pointer Networks instead of Seq2Seq to deal with the dynamic dataset in the above Actor-Critic modules
Devised a Method based on Stochastic Probability called Beam Search for the test function to give more exploration than greedy approach

Itc infotech

Deep learning Research Intern

May 2019 – Jun 2019 · 1 mo · Bangalore

Predicted the fate of a stock (i.e. BUY, SELL or HOLD) using a Reinforcement Learning agent on historical data of 3 different shares
Created several Environments with varying reward functions to obtain the best possible strategy to attain a high Sharpe ratio
Compared the outcome ROI on historical data with Reinforcement learning agents like DQN, Double DQN and neuro-Evolution Strategy
Developed 5 different market indicators namely MA, %R, MACD, RSI, %K and used it to generate a trend using LSTM-RNN to improve ROI
Performed sentiment analysis of news articles using WordtoVec model and Transfer Learning to further improve the sharpe ratio