HARSH VARDHAN VERMA

AI Researcher

Bengaluru, Karnataka, India3 yrs experience

AI EnabledAI ML Practitioner

Key Highlights

2+ years of experience in Data Science and Machine Learning.
Expertise in developing AI-driven solutions for inventory optimization.
Proven ability to drive impact through data-driven decision making.

Stackforce AI infers this person is a Data Scientist specializing in AI-driven solutions within the SaaS industry.

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Skills

Core Skills

Data ScienceMachine Learning

Other Skills

Apache SparkArtificial Intelligence (AI)Azure AIAzure DatabricksC++Cascading Style Sheets (CSS)Data AnalysisData AnalyticsData StructuresData VisualizationDatabricksDeep LearningEDAETLExtract, Transform, Load (ETL)

About

Graduated from IIT Roorkee, currently contributing as a Data Scientist with 2+ years of experience. Worked alongside teams to develop AI-driven solutions leveraging advanced analytics, machine learning, Generative AI, and optimization techniques. Proven ability to drive tangible impact through data-driven solutions in Inventory Optimization, Retrieval Augmented Generation (RAG) systems, Generative AI, Prompt Engineering, Statistical Modeling, Resource Optimization and Structured Data Extraction. Possesses strong technical skills in Python, SQL, machine learning libraries, LLMs, and cloud technologies.

Experience

Pibit.ai

Machine Learning Engineer - II

Aug 2025 – Present · 7 mos · Bengaluru, Karnataka, India · On-site

PythonPySparkSQLDatabricksETLOCR+4

Keepflying®

3 roles

Data Scientist

Jul 2023 – Jul 2025 · 2 yrs

Led end-to-end inventory optimization for an airline client using Python, PySpark, SQL, and Databricks, integrating diverse datasets.
Built automated ETL pipelines with LakeFlow and Spark to process and refresh data daily.
Developed models for safety stock, EOQ, reorder points, FSN-ABC-XYZ classification, and vendor rating to drive data-informed procurement.
Designed and implemented an RAG system to manage and query large-scale airworthiness documents across multiple aircraft and document types.
Built an automated document processing pipeline using OCR from Azure AI Document Intelligence, LangChain, and BAAI/bge-small-en-v1.5 embeddings for document parsing, chunking, and semantic indexing via Azure AI Search.
Engineered a custom hybrid retrieval mechanism combining semantic and keyword search for improved query accuracy and coverage.
Integrated GPT-4o for answer generation with image-based context input and user feedback mechanisms.
Developed hybrid document type identification modules using regex patterns and LLM-based few-shot and role prompting (LLaMA 3.1, GPT-3.5, GPT-4o), achieving 95–98% classification accuracy across text and image inputs for various document types.
Constructed document information extraction pipelines using Azure AI Document Intelligence and prompt-engineered LLMs, achieving 90- 95% structured extraction accuracy across text and image inputs for various document types.
Built an evaluation workflow in MLflow, including a custom metric for assessing structured JSON extraction accuracy, enabling automated benchmarking across models, prompts and document types.