Sharan Vivek Chunamari
Data Engineer @Bank of America
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WORK HISTORY
Data Engineer @Bank of America
NJ, US
Automated data integration process using Apache Airflow to schedule Lambda triggers and manage ETL workflows, streamlining transformation of raw data into categorised Parquet files and reducing overall processing time by 40%- Architected, deployed, and maintained scalable data pipelines on AWS, utilising services such as S3, EC2, Lambda, and Glue, reducing data processing time by 40% and improving pipeline reliability- Optimised ETL scripts and data transformation processes using Python and SQL reducing processing time by 30% and streamlining data workflows- Designed and implemented a secure AWS VPC environment following best security practices, including Security Groups and S3 Endpoints, to ensure network and data protection.
EDUCATION
KLE Technological University - Hubballi (India)
Bachelor of Engineering - BE, Computer Science
Stevens Institute of Technology
Master of Science - MS, Business Intelligence and Analytics
University of Massachusetts Lowell
Entrepreneurship/Entrepreneurial Studies
ABOUT SHARAN VIVEK CHUNAMARI
AI/ML Engineer | LLM, RAG, GenAI | Production ML Systems | Open to Contract Roles (C2C/W2)| Immediate AvailabilityI’m an AI/ML Engineer with 4.5+ years of experience building and deploying production-grade machine learning and Generative AI systems, with a strong focus on NLP, LLMs, and scalable inference pipelines.I specialize in designing end-to-end AI systems—from data processing and feature engineering to model deployment and real-time inference—delivering measurable business impact across enterprise platforms. Core Expertise LLM & Generative AI: Retrieval-Augmented Generation (RAG), prompt engineering, semantic search, transformer-based models (Hugging Face) NLP Systems: Large-scale text processing (5M+ records), document intelligence, summarization, contextual retrieval Production ML: FastAPI-based microservices, scalable inference pipelines (25K+ document chunks/day), low-latency systems MLOps & Deployment: MLflow, CI/CD pipelines, model monitoring, drift detection, experiment tracking Cloud & Data: Azure, AWS, PySpark, distributed data pipelines, vector search infrastructure Key Impact Built LLM-powered document intelligence systems reducing manual validation effort by 40% Improved semantic retrieval accuracy by 20% through optimized embedding & vector search pipelines Designed scalable ML pipelines processing 25K+ document chunks daily with sub-second latency Reduced manual model monitoring effort by 25–30% using automated drift detection frameworks Accelerated experimentation cycles by 30–40% through MLflow and CI/CD integration What Sets Me ApartI combine strong machine learning expertise with production-level software engineering to build scalable, reliable, and business-ready AI systems. My focus is not just on building models—but deploying them effectively in real-world environments with performance, monitoring, and scalability in mind. Currently open to AI/ML contract opportunities across the US (C2C/W2) and available for immediate start. Let’s connect!
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