Resume
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Summary
Data engineering-oriented AI undergraduate with hands-on experience building scalable batch and real-time data pipelines. Strong background in Python, SQL, Spark, Kafka and AWS for ETL, data modeling and analytics-ready systems. Passionate about designing reliable data platforms that support machine learning, accessibility-driven products and large-scale decision systems.
Education
B.Tech, Artificial Intelligence and Data Science 2023 to 2027
Amrita Vishwa Vidyapeetham
Class 12, CBSE 2021 to 2023
Devi Academy Higher Secondary School
Technical skills
- Languages
- Python, Java, C, C++, Scala, Rust, SQL, HTML
- Data engineering
- Apache Spark, Kafka, Hadoop, ETL pipelines, batch and streaming processing, data modeling, Apache Airflow, Snowflake, SQS, Kinesis
- ML and AI
- PyTorch, TensorFlow, Hugging Face, scikit-learn, Pandas, NumPy, MLflow, LangChain, RAG, LangGraph, Pydantic, Ray, Spring Boot, PyArrow
- Databases
- PostgreSQL, MongoDB, Redis, Snowflake, Apache Hive, ChromaDB
- Cloud and DevOps
- AWS (S3, EC2, IAM), Docker, Jenkins, Linux, Terraform, Ansible, Grafana, GitHub Actions, Prometheus, ELK Stack
- Tools
- Git, Jupyter, Power BI, Flutter, FastAPI, REST APIs, Dask, Nginx
Work experience
AI Engineer, Swarm Lens, USA (remote) November 2025 to March 2026
Designed and built scalable big-data pipelines for high-volume ingestion, transformation and validation to support enterprise risk mitigation and data migration. Ensured data quality, fault tolerance and SLA compliance across batch and real-time workloads. Contributed to production deployments, CI/CD automation and operational monitoring, applying MLOps and AIOps practices to improve pipeline reliability and support downstream analytics on cloud-based data platforms.
Projects
Real-Time FinTech Analytics & Risk Monitoring Platform
Built end-to-end streaming pipelines using Apache Kafka and PySpark Structured Streaming with validation, enrichment and deduplication at scale. Engineered ELT transformation workflows in Snowflake with raw and curated data layers in AWS S3 following medallion architecture. Orchestrated hybrid batch and streaming workflows via Airflow with DAG-level monitoring, alerting and retry logic ensuring fault tolerance, SLA adherence and data integrity.
Multilingual RAG Chatbot with Data Ingestion & Moderation Pipelines
Designed scalable batch and real-time pipelines for multilingual document ingestion, preprocessing and moderation as the data backbone for a RAG system. Implemented an embedding generation pipeline integrated with hate speech detection as a data quality filtering stage, ensuring only validated data reached the vector store. Optimized retrieval-path data flows for low-latency inference and deployed infrastructure with cloud-ready configurations on AWS, with end-to-end observability via CloudWatch.
See all projects, including the accessible e-commerce platform.