Vaibhav Sharma
Machine Learning Engineer & Software Developer
Building intelligent solutions and crafting elegant code to solve complex problems
About Me
I am a passionate Machine Learning Engineer and Software Developer with a strong foundation in AI/ML, big data, and scalable systems. I enjoy building intelligent solutions, exploring new technologies, and contributing to open-source projects. My work spans research, engineering, and deployment of ML systems at scale.
Education
MS, Data Science
Indiana University Bloomington, USA
GPA: 3.8/4.0
Relevant Coursework: Applied Machine Learning, Data Mining, Statistics, Reinforcement Learning, Advanced Database Concepts, Applied NLP
Quick Facts
- Passionate about AI/ML and Software Development
- Strong background in both theoretical and practical aspects
- Always learning and exploring new technologies
- Open to collaboration and new opportunities
Skills & Expertise
Languages
Python95%
Java85%
Scala80%
SQL90%
Shell80%
Frameworks & Libraries
PyTorch90%
TensorFlow90%
scikit-learn90%
MLlib80%
Hugging Face85%
Langchain80%
OpenAI API80%
GPT-480%
Big Data & ML Infra
Apache Spark90%
Hadoop80%
Airflow85%
Delta Lake80%
Kafka80%
Hive75%
Databricks85%
MLFlow80%
Cloud & Deployment
AWS (SageMaker, Lambda)85%
GCP (Vertex AI)80%
AzureML75%
Docker85%
Kubernetes75%
MLFlow80%
Systems & Tools
Spring Boot70%
Flask85%
RESTful APIs90%
CI/CD80%
Techniques
NLP90%
Time Series85%
Forecasting80%
Recommendation Systems85%
A/B Testing80%
PCA75%
Causal Inference70%
Embedding Models80%
LLM fine-tuning80%
Prompt engineering80%
Professional Experience
Google Summer of Code | Open-source AI Developer
Apr 2025 – PresentRemote, USA
- Building a research paper contributions reproduction, mapping and evaluation open-source tool using AI agents.
- Leveraged retrieval pipelines and prompt chaining strategies to interpret and map research contributions to modular code components.
- Worked on integrating multimodal inputs into LLM workflows for structured content understanding and alignment.
Radical AI | AI Engineer
Jun 2024 – Aug 2024Remote, USA
- Designed and deployed worksheet generator for educators using RAG architecture and LangChain, optimizing data retrieval pipelines using GCP Vertex AI API.
- Integrated evaluation metrics for Question-Answering system for comparing fine tuning vs RAG performance.
Inmobi | ML Engineer
Apr 2021 – Jul 2023Bangalore, India
- Optimized inference latency of a lookalikes ML recommendation engine by 67% through systematic experimentation with Spark query plans and caching strategies, significantly improving online performance.
- Designed augmentation algorithm over internal identity graphs to enrich user profiles, enabling efficient user ID transactions to Cosmos DB and improving CTR by 20% while reducing RU consumption by 40%.
- Integrated hyper-log-log data structure in DB storage model and Spark jobs for ETL framework mitigating data availability delay issues and reducing compute costs by 30%.
- Evaluated BERT embeddings vs traditional LDA for topic modelling on app descriptions; deployed a hybrid NLP model that improved coherence scores by 60%, enhancing semantic relevance in recommendations.
- Redesigned big data pipeline quality checks with automated data validation and dashboarding, reducing manual QA efforts by 50% and ensuring reliable monitoring at scale.
- Built threshold-based forecasting models on high-dimensional user activity data; reduced feature space by 80% while preserving model accuracy, enabling scalable deployment for downstream tasks.
- Developed an ensemble model combining LightGBM and TF-IDF features for user lookalike classification, achieving 20% offline ROC-AUC lift and 25% improvement in business KPIs during A/B testing.
- Modernized the A/B testing framework implementation and pipelines for performance benchmarking reducing manual efforts by 50%.
Fintech Unicorn (CRED) | Data Scientist
Jul 2020 – Mar 2021Remote
- Achieved 87% accuracy in forecasting the peak signups & bureau fetches on the CRED app during high-demand periods in 2021 using ARIMA.
- Built pipelines for real-time monitoring and analysis of traffic spikes to support business insights and load balancing.
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