JUNAID AHMED

• Leading the development of a contract analysis engine using QLoRA-based fine-tuning on Databricks for legal clause
classification, redlining, and risk detection.
Fine-tuned LLaMA2/Mistral models using legal domain data, with LoRA adapters and PEFT (Parameter-Efficient Fine
Tuning).
Built clause-specific training datasets for approximately 12 clause types (Confidentiality, Termination, Liability, etc.). With
RAG-based template retrieval.
Integrated FAISS-based similarity search and LangChain pipelines for clause comparison and automated feedback
against internal playbooks.
Deployed inference workflows using Databricks Unity Catalog, evaluated using custom clause-level accuracy, and F1
metrics.
• Architected and implemented a market share analytics platform for 2-wheeler products: Developed an end-to-end, Python
based system to compute market share changes across categories and time periods (3M, 6M, 12M).
Integrated competitor benchmarking and delta trends, outlier detection by CC range, and visual alerts using Power BI and
Streamlit.
Built modular SQL pipelines and batch scripts to automate monthly data ingestion from regional sales.
• Mentored two interns and one junior data scientist: Trained interns on foundational NLP techniques, data preprocessing,
and Databricks workflows.
Reviewed code, established Git-based workflows, and led pair-programming sessions for fine-tuning experiments.
Conducted weekly syncs and milestone reviews to ensure delivery alignment and learning progression.
• Collaborated cross-functionally with legal, sales, and product teams to translate domain problems into scalable ML
solutions.
• Contributed to internal GenAI strategy by evaluating cost-performance trade-offs between base and fine-tuned LLMs in
RAG systems.

As an ML engineer worked on NLP projects in addition to Data engineering POC's and Power BI reporting