refactor experiences and projects data; update roles, responsibilities, and descriptions for clarity and accuracy

Author: TechPertz

app/data/experiences.ts

@@ -10,42 +10,43 @@ export interface Experience {
 export const experiences = {
     technical: [
       {
-        company: "Backend Developer Intern (AI/ML)",
-title: "Mobility Intelligence Inc",
-        period: "June 2024 - December 2024",
+        company: "Mobility Intelligence",
+        title: "FullStack Development Intern",
+        period: "June 2024 – December 2024",
         location: "New York City, USA", 
-        techStack: ["Python", "SQL", "Apache Airflow", "Flask"],
+        techStack: ["Python", "FastAPI", "Celery", "Redis", "PostgreSQL", "Apache Airflow", "Prometheus", "Grafana", "AWS", "Kubernetes"],
         responsibilities: [
-          "Developed a <b>Django-ReactJS</b> platform integrating ML models, delivering real-time predictions with <b>sub-1 second latency</b> through optimized APIs.",
-          "Implemented regression models for custom price prediction over a 90-day horizon, leveraging TimeSeriesSplit and hyperparameter optimization to reduce errors within +/- 5%.",
-          "Engineered <b>ETL pipelines</b> with Apache Airflow, processing <b>15M+ records daily</b> and automating transformations, saving 3 man hours per dataset.",
-          "Optimized PostgreSQL queries to support frequent API calls and large-scale workflows, improving data retrieval efficiency."
+          "Built a real-time price prediction system using regression and Kalman filtering, achieving less than 5% error on 90-day forecasts.",
+          "Designed a FastAPI backend with Celery and Redis, handling 150k requests daily with 99.9% uptime and sub-500ms P95 latency.",
+          "Scheduled Airflow DAGs managing ETL pipelines processing 15M+ daily records from PostgreSQL into analytics-ready stores.",
+          "Configured Prometheus + Grafana with SLIs and alerting rules, reduced MTTD by 60% and improved response workflows.",
+          "Deployed microservices in AWS using Kubernetes with Helm charts, rolling updates, and horizontal pod autoscaling, reducing downtime during deployments by 80% and enabling seamless CI/CD."
         ]
       },
       {
-        company: "Software Engineering Intern (R&D)",
-        title: "Defence Research & Development Organisation (Govt. of India)",
-        period: "January 2023 - June 2023",
-        location: "Chandigarh, India", 
-        techStack: ["Python", "Shell", "PyQT", "MatPlotLib", "SerialPy", "SQL", "Docker"],
+        company: "Defence Research & Development Organisation",
+        title: "Software Engineering Intern (R&D)",
+        period: "January 2023 – June 2023",
+        location: "India", 
+        techStack: ["Python", "Redis", "PostgreSQL", "PostGIS", "LiDAR", "ETL"],
         responsibilities: [
-          "Advised the Assistant Director on a project to estimate heavy vehicle integrity using LiDAR and GPS sensors, selected as the sole contributor out of 20+ interns.",
-          "Designed and developed a Python desktop application to reverse-engineer RS232 serial ports, decoding 1.5M+ bytes/sec from LiDAR sensors and applying 25+ custom algorithms for rut measurement analysis and visualization.",
-          "Published a Windows desktop application with 20K+ lines of code optimized to remain under 30MB, ensuring portability and performance in resource-constrained environments."
+          "Engineered multithreaded architecture for real-time LiDAR processing, handling 50K data points/sec (97% accuracy).",
+          "Implemented Redis-based geospatial caching over PostgreSQL/PostGIS, reducing GPS query latency from 1000ms to 150ms.",
+          "Developed ETL pipeline using memory-efficient streaming, processing 12GB/min while reducing memory usage by 60%."
         ]
       },
       {
-        company: "Software Engineering Intern (Backend)",
-        title: "Solar Industries India Ltd",
-        period: "April 2022 - December 2022",
-        location: "Mumbai, India", 
-        techStack: ["Django", "FastAPI", "AWS", "Docker", "Jenkins", "Kafka", "Redis", "Elasticsearch"],
+        company: "Solar Industries India Ltd",
+        title: "Software Engineering Intern",
+        period: "April 2022 – December 2022",
+        location: "India", 
+        techStack: ["Django", "Kafka", "Redis", "Cassandra", "Jaeger"],
         responsibilities: [
-          "Led a team of 5 interns to automate workflows across 25+ industrial plants, delivering 5 Python-based projects with 75K+ lines of code, deployed using Docker and orchestrated with Kubernetes.",
-          "Engineered scalable backend systems processing 100K+ API requests/day and 2.5M+ rows/sec, ensuring fault-tolerance with Cassandra replication, Kafka partitioning, and caching with Redis.",
-          "Optimized infrastructure by implementing Kubernetes orchestration, distributed CRON jobs, and caching layers, reducing API response times by 25% and monitored system health with Prometheus."
+          "Led a team of 5 to automate workflows, delivering 5 Django microservices that standardized 80% of manual processes.",
+          "Reduced API latency by 25% and integrated distributed tracing with Jaeger, enabling real-time debugging.",
+          "Designed a partitioned Kafka pipeline with scalable consumer groups, processing 2.5M+ rows/sec using Redis caching and Cassandra-backed storage."
         ]
-      }, 
+      },
       {
         company: "Full Stack / AI Developer",
         title: "Freelancer / Contract Jobs",

app/data/projects.ts

@@ -14,10 +14,10 @@ export const projects: Project[] = [
     code: "DocFlow",
     title: "DocFlow: GraphRAG - LLM Document Compliance",
     description: [
+      "Launched an Agentic SaaS with real-time document edit, approval and audit reports via Graph based RAG and LLM.",
       "Implemented a GraphRAG and PDF parser from scratch for unstructured PDFs using PDFMiner & Tesseract (OCR).",
       "Executed semantic chunking + NLP NER + BART-CNN summarization, achieving 70% relation extraction accuracy.",
-      "Synthesized hybrid retrieval (Vector + Graph + metadata) boosting compliance accuracy to 90%.",
-      "Launched Next.js SAAS with real-time document edit, approval and audit reports via WebSocket and Anthropic API."
+      "Synthesized hybrid retrieval (Vector + Graph + metadata) boosting compliance accuracy to 90%."
     ],
     tags: [
       "Next.js",
@@ -37,9 +37,9 @@ export const projects: Project[] = [
     code: "VectorFlow",
     title: "VectorFlow: Hierarchical Vector Database (from Scratch)",
     description: [
-      "Architected hierarchical vector database (library → document → chunk) with async per-collection mutexes; 12K ops/sec, 0.1% lock contention.",
-      "Engineered hybrid index backends (LinearScan/KD-Tree/LSH) for O(1)-O(n) search; 18ms latency across 10M vectors.",
-      "Deployed race-free Kubernetes pipelines (Helm/Minikube) with CLI toolkit, reducing onboarding complexity by 100%.",
+      "Built embedding database (library - document - chunk) with async collection mutexes; 12K ops/sec at <0.1% conflicts.",
+      "Added 3 indexing algorithms (LinearScan/KD-Tree/LSH) for vector search on 10M vectors in 18ms.",
+      "Led Kubernetes Helm deployment along with custom made CLI toolkit, reducing onboarding complexity by 100%."
     ],
     tags: [
       "Python",
@@ -53,9 +53,9 @@ export const projects: Project[] = [
       "Minikube",
     ],
     domain: ["AI", "Backend", "Core/Systems", "Cloud", "DevOps/Infra"],
-    link: "",
-    isLinkClickable: false,
-    linkText: "AI2Web3 Bootcamp Top Project",
+    link: "https://www.github.com/techpertz/vectorflow",
+    isLinkClickable: true,
+    linkText: "View on GitHub",
   },
   {
     code: "GrantGenie",
@@ -129,8 +129,9 @@ export const projects: Project[] = [
     code: "Whiteboard",
     title: "White board: Real-Time Collaborative Java Application",
     description: [
-      "Designed and implemented a real-time collaborative whiteboard application, leveraging WebSockets for seamless, multi-user drawing updates and Java Swing for an intuitive GUI.",
-      "Optimized performance with batch processing, asynchronous communication, and thread-safe concurrency controls, ensuring low-latency updates and efficient handling of large-scale data.",
+      "Built low-latency collaboration using Spring Boot and Swing, achieving <150ms sync for concurrent users via binary compression.",
+      "Implemented vector operations using operational transformation, resolving 98% conflicts in real-time updates.",
+      "Executed socket programming with PostgreSQL and JSONB storage, achieving 85% network overhead reduction."
     ],
     tags: ["Java", "Java Swing", "Spring Boot", "WebSockets", "Batch Processing"],
     domain: ["Frontend", "Backend", "Core/Systems"],