style(resume): remove redundant markdown formatting

- Eliminate unnecessary bold markdown in project highlights
- Maintain consistent formatting across all project descriptions
- Ensure readability and uniformity in resume presentation

These changes streamline the visual presentation of the resume, enhancing clarity and professionalism by removing redundant markdown elements.

Author: subhayu99

resume.yaml

@@ -41,15 +41,15 @@ cv:
       start_date: "2022-06"
       summary: "Delivered end-to-end data and AI solutions for 10+ enterprise clients across healthcare, finance, logistics, and tech sectors. Responsibilities extended beyond technical delivery: conducted 100+ technical interviews, led client onboarding and scoping calls, created PoCs/demos, mentored teammates, and drove adoption of emerging technologies (DuckDB, MCP) across the organization.\\ **Client Engagements:**"
       highlights:
-        - "**SBGC Group (Retail Analytics)**: Took ownership of a multi-region Azure/Databricks platform integrating SAP/Shopify/Klaviyo/GA4 to feed Hightouch CDP. Unblocked Advent International due diligence by reverse-engineering Shopify's logic to cut revenue variance from ~10% to \\<0.22%. Root-caused a critical legacy data explosion (slashing volume 99%) and delivered an inventory dashboard worth ~$250k."
-        - "**AGY Logistics (IoT & Automation)**: Sole architect of a GDP-compliant cold chain system on GCP (BigQuery, Cloud Run). Automated complex manual workflows previously requiring 10-15 US-based operations staff, resulting in massive OpEx savings. Engineered smart time-series merging and statistical analysis on a **4-layer medallion architecture (including a 'Diamond Layer' for sub-second alerts)** to reduce false positives by 70%."
-        - "**Johnson & Johnson (Healthcare CDP)**: Architected scalable Treasure Data pipelines for regulated healthcare markets (JP/ANZ). Engineered complex transformation logic, including a **12-scenario SQL truth table to resolve hierarchical consent conflicts** and a custom Python engine to overcome SFMC platform limitations, ensuring strict GDPR/PII compliance."
-        - "**Wade Insight (Cloud Migration)**: Led enterprise migration from ADF to Microsoft Fabric Data Factory, managing ARM template adaptation. Enhanced the SaaS platform with advanced `continue_on_failure` orchestration and automated health-check processes, reducing manual troubleshooting by 80%."
-        - "**Prospexs (GenAI Product Engineering)**: Built an AI outreach platform (Python/FastAPI/MongoDB) featuring a **complex 4-entity personalization engine** (Sender/Receiver × Human/Company). Integrated OpenAI/Perplexity APIs to generate bilingual, context-aware communications, improving response rates by 45%."
-        - "**QxLab (LLM Infrastructure)**: Led a 4-person team fine-tuning Llama/Mistral models using Axolotl on H100 clusters to achieve \\>95% accuracy in production agentic system. Separately, architected a Terabyte-scale **cascading deduplication pipeline (MinHash LSH + FAISS)** to standardize datasets (SFT/DPO/RLHF), solving critical bottlenecks. Later released as the open-source project (DatasetPipeline)."
+        - "**SBGC Group (Retail Analytics)**: Took ownership of a multi-region Azure/Databricks platform integrating SAP/Shopify/Klaviyo/GA4 to feed Hightouch CDP. Unblocked Advent International due diligence by reverse-engineering Shopify's logic to cut revenue variance from ~10% to \\<0.22%. Root-caused a critical legacy data explosion (slashing volume by 99%) and delivered an inventory dashboard worth ~$250k."
+        - "**AGY Logistics (IoT & Automation)**: Sole architect of a GDP-compliant cold chain system on GCP (BigQuery, Cloud Run). Automated complex manual workflows previously requiring 10-15 US-based operations staff, resulting in massive OpEx savings. Engineered smart time-series merging and statistical analysis on a 4-layer medallion architecture (including a 'Diamond Layer' for sub-second alerts) to reduce false positives by 70%."
+        - "**Johnson & Johnson (Healthcare CDP)**: Architected scalable Treasure Data pipelines for regulated healthcare markets (JP/ANZ). Engineered complex transformation logic, including a 12-scenario SQL truth table to resolve hierarchical consent conflicts and a custom Python engine to overcome SFMC platform limitations, ensuring strict GDPR/PII compliance."
+        - "**Wade Insight (Cloud Migration)**: Led enterprise migration from ADF to Microsoft Fabric Data Factory, managing ARM template adaptation. Enhanced the SaaS platform with advanced continue-on-failure orchestration and automated health-check processes, reducing manual troubleshooting by 80%."
+        - "**Prospexs (GenAI Product Engineering)**: Built an AI outreach platform (Python/FastAPI/MongoDB) featuring a complex 4-entity personalization engine (Sender/Receiver × Human/Company). Integrated OpenAI/Perplexity APIs to generate bilingual, context-aware communications, improving response rates by 45%."
+        - "**QxLab (LLM Infrastructure)**: Led a 4-person team fine-tuning Llama/Mistral models using Axolotl on H100 clusters to achieve \\>95% accuracy in production agentic system. Separately, architected a Terabyte-scale cascading deduplication pipeline (MinHash LSH + FAISS) to standardize datasets (SFT/DPO/RLHF), solving critical bottlenecks. Later released as the open-source project (DatasetPipeline)."
         - "**CV Advisors (Performance Engineering)**: Replaced a 1900-line legacy SQL procedure (iterative cursors) into vectorized logic (DuckDB/Pandas), processing 80M+ records across 150 clients. Reduced runtime from 27.5h to \\<5s (\\>99.9% gain). Engineered a future-proof architecture capable of scaling beyond RAM limits via memory-optimized categorical typing and DuckDB’s out-of-core processing."
         # - "**Logical Contract (Legal Tech)**: Implemented an AI-powered legal tech system for generating tailored employment agreements and a legal chatbot for startup inquiries."
-        - "**SlideNinja (GenAI RAG Architecture)**: Developed a GenAI RAG platform **(LangChain/ChromaDB)** for a McKinsey partner during the early GenAI boom, featuring a 'Self-Healing' AI orchestration layer and proprietary 'Geometric Layout Analysis' to map unstructured content into rigid corporate PowerPoint templates."
+        - "**SlideNinja (GenAI RAG Architecture)**: Developed a GenAI RAG platform (LangChain/ChromaDB) for a McKinsey partner during the early GenAI boom, featuring a 'Self-Healing' AI orchestration layer and proprietary 'Geometric Layout Analysis' to map unstructured content into rigid corporate PowerPoint templates."
         - "**LoopKitchen (Real-time Data Ingestion)**: Built core data architecture (GCP/BigQuery/FastAPI) enabling a $6M Series A. Migrated fragile legacy scrapers to robust official API integrations (UberEats/DoorDash/Grubhub) for millions of orders and engineered an automated dispute resolution system to directly recover lost revenue."
 
     - company: "FiftyFive Technologies"
@@ -186,9 +186,9 @@ cv:
         date: "Nov 2025"
         show_on_resume: true
         highlights:
-          - "Created a zero-setup SQL tool to quickly analyze data files (CSV, Parquet, Markdown, HTML, etc.) via a **CLI** or **Python library**, streamlining repetitive data quality checks without database overhead."
-          - "Built a powerful **interactive shell** with syntax highlighting and history for data exploration, alongside a standard query mode for piping results into other terminal tools."
-          - "Engineered a flexible system that auto-switches between **DuckDB**, **Pandas**, or native Python to execute queries, implementing smart optimizations to filter data before loading; published on [PyPI](https://pypi.org/project/sqlstream) with full [documentation](https://subhayu99.github.io/sqlstream)."
+          - "Created a zero-setup SQL tool to quickly analyze data files (CSV, Parquet, Markdown, HTML, etc.) via a CLI or Python library, streamlining repetitive data quality checks without database overhead."
+          - "Built a powerful interactive shell with syntax highlighting and history for data exploration, alongside a standard query mode for piping results into other terminal tools."
+          - "Engineered a flexible system that auto-switches between DuckDB, Pandas, or native Python to execute queries, implementing smart optimizations to filter data before loading; published on [PyPI](https://pypi.org/project/sqlstream) with full [documentation](https://subhayu99.github.io/sqlstream)."
       - name: "DatasetPipeline"
         date: "May 2025"
         show_on_resume: true