guest@mysura:~ $ cat profile.md
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# Mysura Reddy Kuchuru
Data Engineer · Analytics Engineer · Data Analyst · AI/ML systems direction
I build reliable pipelines, dimensional models, quality controls, and decision layers—then create responsible paths for AI and ML on top of governed data.
Pipelines, APIs, SQL models, cloud foundations, data quality, and documentation that make downstream analytics and ML safer to use.
messy sources · drifting schemas · unclear KPIs · AI without context
contracts · models · observability · dashboards · evaluated intelligence
guest@mysura:~ $ head -n 3 featured.systems
Completed work first. Future capabilities are labeled as roadmaps—not quietly smuggled in as buzzwords.
Python ingestion, PostgreSQL layers, dimensional modeling, dbt documentation and tests, analytics views, indexing, and Tableau decision support across 14.9M raw trips.
open repository ↗Serverless batch engineering with AWS Glue, PySpark, S3, IAM, and CloudFormation.
open repository ↗A tested, dependency-free quality gate for analytics datasets and ML features.
open repository ↗guest@mysura:~ $ neofetch --data-stack
Python · SQL · PostgreSQL · Pandas · ETL/ELT · APIs · dimensional modeling
dbt · semantic metrics · data contracts · testing · documentation · lineage direction
Exploratory analysis · reconciliation · KPI reporting · Tableau · Power BI · Excel
AWS · GCP · Docker · Kubernetes · CloudFormation · GitHub Actions · Git
LLM applications · RAG · NLP · semantic search · feature quality · responsible evaluation
#Python #SQL #PostgreSQL #dbt #Pandas #Tableau #PowerBI #AWS #GCP #Docker #Kubernetes #OpenAI #RAG #NLPguest@mysura:~ $ cat experience.log
Claims and recovery analytics, reconciliation, dashboard automation, KPI reporting, and process improvement.
LLM and NLP applications, Flask APIs, cloud automation, and enterprise dashboards.
Java and SQL development, API integrations, AWS foundations, and enterprise engineering practices.
guest@mysura:~ $ ls -la ~/projects/
Word counts, bigrams, stop-word filtering, and inverted indexing with Python and mrjob.
#big-data #text-analytics #mapreduceResponsible web-to-dataset collection producing structured CSV and JSONL for analysis and NLP.
#etl #beautifulsoup #nlp-roadmapTested scheduling metrics connected to batch processing and shared ML compute concepts.
#algorithms #testing #ml-infrastructureAn interpretable online-learning baseline that updates behavior from observed moves.
#online-learning #statistics #pythonStreaming metrics and anomaly-rule processing foundations for typed .NET data services.
#dotnet #stream-processing #mlnet-roadmapResponsible RevOps infrastructure documentation with governed analytics and AI extensions.
#revops #governance #responsible-aiThe source system for this terminal résumé, accessibility layer, and project narrative.
#github-pages #frontend #portfolioDefensive MITM research connected to trustworthy data movement and model-serving integrity.
#data-security #ai-security #researchguest@mysura:~ $ render architecture.graph
The future-facing architecture is grounded in a simple rule: earn the right to use AI by making the data trustworthy first.
NOTE: ML, RAG, and agent components are a forward roadmap. Current delivered capabilities remain documented inside each repository.
guest@mysura:~ $ cat education.json
New York Institute of Technology
Jawaharlal Nehru Technological University
guest@mysura:~ $ verify credentials --focus data,cloud,ai
A curated signal from verified learning—not a wall of badges. Each credential reinforces the systems behind modern analytics and AI.
Qwiklabs · Google Cloud learning ecosystem
verify credential ↗ 02 / ANALYTICS⌁Qwiklabs · analytical querying
verify credential ↗ 03 / CLOUD△Qwiklabs · scalable infrastructure
verify credential ↗ 04 / ORCHESTRATION⬡Qwiklabs · container orchestration
verify credential ↗ 05 / DELIVERY∞Qwiklabs · reliable delivery
verify credential ↗ 06 / PLATFORM◈Qwiklabs · platform fundamentals
verify credential ↗guest@mysura:~ $ query linkedin.activity --topic data-ai
#vector-search · #databases · #genai
A practical look at building AI-ready applications with semantic search, recommendations, pgvector, LangChain, and Hugging Face—without separating operational and vector data.
read the post on LinkedIn ↗DATA NOTE: This section highlights verified writing and recommendations only. Private LinkedIn analytics are intentionally not displayed.
guest@mysura:~ $ tail -n 2 recommendations.log
Independent signals about analytical thinking, applied AI, ownership, and reliable execution.
Exceptional analytical skills and a strong understanding of machine learning concepts.
Proactive in asking the right questions—and executes tasks to a high standard and on time.
guest@mysura:~ $ cat roadmap.yml
Testing, documentation, contracts, reproducibility, and observable delivery.
Orchestration, lakehouse patterns, semantic metrics, and controlled experimentation.
Feature monitoring, evaluated RAG, responsible agents, and human-centered AI.
guest@mysura:~ $ cat ~/.profile
export LOCATION="Charlotte, North Carolina"
export ROLES="Data Engineer | Analytics Engineer | Data Analyst"
export EMAIL="kuchurumysurareddy@gmail.com"
guest@mysura:~ $