Shardul Karanjekar
@shardulkaranjekar
I build applied NLP, RAG, and computer vision systems with measurable ML outcomes.
What I'm looking for
At Infosys, I fine-tuned FinBERT for financial NER on SEC filings and built a PyTorch and pdfplumber pipeline for PDF ingestion, section detection, inference, and structured JSON output.
I've also built a fully offline Indian tax-compliance RAG system using Gemma, FAISS, and RAGAS evaluation, alongside e-commerce analytics and churn prediction products deployed through FastAPI dashboards.
My M.Tech thesis applies ControlNet diffusion to adaptive camouflage synthesis, while my computer vision work spans PatchCore anomaly detection, YOLOv8 steel-defect detection, and TensorRT inference optimization. I'm seeking applied NLP and GenAI work where I can ship practical ML systems.
Experience
Work history, roles, and key accomplishments
Curated a financial NER dataset with 222 documents and 8,652 labeled entities across 11 types. Fine-tuned FinBERT on SEC filings and built a PyTorch pipeline for PDF ingestion and NER inference.
Python Expert
Uber AI Solutions
Apr 2025 - Sep 2025 (5 months)
Designed 15+ LLM evaluation criteria and scored 250+ model outputs for relevance, coherence, and alignment. Feedback directly shaped prompt-engineering and fine-tuning priorities.
Intern
ZCEnergie Solutions
Jan 2022 - Apr 2022 (3 months)
Analyzed 200K+ rows of SCADA data from 82 sensors for anomaly detection. Partnered with domain SMEs on FMEA to convert Risk Priority Numbers into maintenance scheduling inputs.
Education
Degrees, certifications, and relevant coursework
Defence Institute of Advanced Technology
Master of Technology, Data Science
2024 -
Grade: 8.42/10
Pursuing Master of Technology in Data Science with a GPA of 8.42/10. Coursework includes machine learning, deep learning, computer vision, and data structures.
PVG College of Engineering, Pune
Bachelor of Engineering, Electrical Engineering
2019 - 2023
Grade: 8.07/10
Completed Bachelor of Engineering in Electrical Engineering with a minor in AI/ML, achieving a GPA of 8.07/10. Coursework included IoT, electric vehicles, and power electronics.
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