I'm an AI Engineer and Data Engineer passionate about building intelligent agent systems, scalable data solutions, and enterprise-grade applications. I specialize in designing multi-agent orchestration systems, RAG architectures, and production-ready AI pipelines — powered by a strong foundation in Data Engineering, Cloud Technologies, and Microservices.
- 🤖 AI/ML & Agents — Multi-agent orchestration, RAG systems, LangGraph, Google ADK, and intelligent automation
- 🔷 Data Engineering — Robust data pipelines, ETL/ELT systems, data warehousing & lake-house architectures
- ☁️ Cloud Engineering — Multi-cloud (AWS, Azure, GCP) with Terraform, IaC, and serverless architectures
- ☕ Enterprise Python,Java & Scala — Python (FastAPI),Python with ML ,Java/JEE, Spring ecosystem, Scala, Microservices, and distributed systems
- ✅ AWS Certified Machine Learning — Specialty (2025)
- ✅ AI Agents Fundamentals — Hugging Face (2025)
- ✅ Databricks Certified Data Engineer Professional (2024)
- ✅ Databricks Certified Data Engineer Associate (2024)
- ✅ HashiCorp Certified: Terraform Associate (2022)
- ✅ Microsoft Certified: Azure Data Engineer Associate (2022)
- ✅ GridGain Developer Essentials: Apache Ignite & Spring (2022)
- ✅ Google Cloud Certified Professional Data Engineer (2021)
- ✅ AWS Certified Solutions Architect — Associate (2021)
- ✅ Microsoft Certified: Azure Fundamentals (2021)
Languages & Core:
Python · Scala · SQL · Java · Shell · JavaScript
AI/ML & Agents:
LangChain · LangGraph · Google ADK · Composio · LiteLLM · Google A2A · Microsoft Agent SDK · OpenSearch · Vector Databases
Python & ML Engineering:
FastAPI · FastMCP · SQLAlchemy · Scikit-learn · TensorFlow · SageMaker · Pandas · NumPy · Pickle (pkl) · Model Training · Model Deployment · Model Serving · Model Evaluation
Data Engineering:
Apache Spark · Spark Streaming · Kafka · Apache Flink · Flink Streaming · Hive · MapReduce · Oozie · Airflow · Snowflake · BigQuery · Redshift · AWS Glue · Azure Data Factory · Dataflow · dbt
Cloud & Infrastructure:
AWS · Azure · GCP · Terraform · Docker · Kubernetes · Cloud Run · Lambda · CloudFormation
Databases:
MySQL · PostgreSQL · MongoDB · Cassandra · DynamoDB · Redis
Java/JEE & Scala:
Java · JEE · Spring · Spring Boot · Spring ORM · Hibernate · JPA · JSP · Servlets · EJB · Scala · Microservices · REST APIs · Maven · Gradle
- 🔬 AI Agent Systems — Multi-agent orchestration, A2A communication, and autonomous agent frameworks
- 📊 Document Intelligence — Advanced OCR, Docling experiments, and document understanding
- 🛠️ MCP Servers — Model Context Protocol servers for databases, APIs, and vector databases
- 🚀 LangGraph Implementations — Financial intelligence, HITL systems, agentic RAG, and adaptive flows
- 🌍 Cloud-Native AI — GenAI solutions with AWS Bedrock, OpenSearch RAG, and serverless deployments
- ⭐ GCP_Data_Enginner_Utils — GCP_Data_Enginner
Shell - ⭐ Databricks-GenAI — —
Jupyter Notebook - ⭐ ApacheFlink_Utils_Prv — Apache Flink Utils
Java - ⭐ docling_ocr_experiments — Docling Experiments
Jupyter Notebook - ⭐ order_support_orchestrator_agents — Order support Orchestrator Agents (A2A) agents for customer supports
Python - ⭐ A2A_Travel_agents — —
Python - ⭐ Awesome_Agents — Awesome_Agents SDK (its contains RAG Builders and Agents Builders SDK) using multiple Agents framework
Jupyter Notebook - ⭐ Databricks_UnitCatalog — —
Jupyter Notebook
🤖 AI Agents & Orchestration (45 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | Summoner_A2A | Python | Google A2A with Summoner apporach From summoner as Orchestor agent calling other A2A agents (earth a2a server, fire,water) 3 a2a servers |
| 2 | strands_agents_aws | Shell | Experiments with strands agents |
| 3 | order_support_orchestrator_agents | Python | Order support Orchestrator Agents (A2A) agents for customer supports |
| 4 | financial-intel-langgraph-agent | — | financial-intel-langgraph-agent |
| 5 | agentcon-pizza-workshop | — | agentcon pizza workshop using Google ADK and Microsoft agent sdk |
| 6 | composio_orchestrate_exp | — | composio orchestrate experiment's |
| 7 | custom_ai_agent_framework | — | Custom AI agents Framework no |
| 8 | orchestrator_flows | Python | orchestrator |
| 9 | A2A_Travel_agents | Python | — |
| 10 | a2a_order_policy_docs | Python | A2A. order policy documents processing engine |
| 11 | Langgraph_AgentMiddleware | — | — |
| 12 | deep_agents_experiments | Jupyter Notebook | Deep agents utils |
| 13 | textract-llm-agent | Python | Textract LLM agent system @LLM @agents @Langgraph @AWS Textract @Lambda @DyanmoDB |
| 14 | huggingface_smolagents_utils | Python | smolagents |
| 15 | data_engineer_agents_setup | — | Data Engineering with Agents setup for pre and post process |
| 16 | ai_agents_retailer_orders | — | Order status Agentic System |
| 17 | A2A_Examples | Python | A2A ,MCP exampels |
| 18 | mindsdb_exp_a2a | Python | Query Engine for AI - The only MCP Server you'll ever need |
| 19 | production-grade-agentic-system | Python | Core 7 layers of production grade agentic system |
| 20 | ADK_MCP_websocket_fastapi | HTML | — |
| 21 | ADK_MCP_websocket_fastapi-multiple-mcps | Python | — |
| 22 | amazon-bedrock-agentcore-utilits | Jupyter Notebook | Amazon Bedrock Agentcore accelerates AI agents into production with the scale, reliability, and security, critical to real-world deployment. |
| 23 | Kylie_whatsup_agent | Python | Whatsap Agent |
| 24 | vector-ai-agents-lab | Python | vector-ai-agents-lab |
| 25 | Awesome_Agents | Jupyter Notebook | Awesome_Agents SDK (its contains RAG Builders and Agents Builders SDK) using multiple Agents framework |
| 26 | agents-langgraph-workshop-dhs2025 | Jupyter Notebook | This repository will contain all the presentations, content, hands-on python notebooks for a full day Agentic AI workshop on Building Simple and Complex Agents, Deploying and Monitoring AI Agents w… |
| 27 | AIPC-ADK-Agents | Python | — |
| 28 | stock_trading_agents | — | Stock trading app |
| 29 | a2a_demo | Python | This repository contains base working directory for Codelab: Getting Started with Agent-to-Agent (A2A) Protocol: A Purchasing Concierge and Remote Seller Agent Interactions with Gemini on Cloud Run |
| 30 | ai-agents-in-action-book | — | AI agents in action book |
| 31 | adk-simple | Python | Google Agent Development KIT |
| 32 | A2A | Python | An open protocol enabling communication and interoperability between opaque agentic applications. |
| 33 | fastapi-langgraph-agent-production-ready-template | Python | A production-ready FastAPI template for building AI agent applications with LangGraph integration. This template provides a robust foundation for building scalable, secure, and maintainable AI agen… |
| 34 | Learning_New_agents_info | — | Learning New |
| 35 | Magentic_Autogenstudio_Utils | Python | Autogen Magentic |
| 36 | AI_Agent-_Evaluation_Databricks | Jupyter Notebook | AI agents evaluation using databricks |
| 37 | huggingface_agents | — | Huggingface agents |
| 38 | Agents_training_Berkeley_RDI- | — | — |
| 39 | AWS_AI_Agents_bedrock | — | Agents in AWS bedrock |
| 40 | Agents_Comparison | — | — |
| 41 | AgentEval_understanding | Python | Agent Evaluation |
| 42 | AI_Agents_Utils | — | AI Agents Utils |
| 43 | OpenOmniFramework | Python | Multimodal Open Source Framework for Conversational Agent Research and Development. |
| 44 | LLM_Langgraph_Agents | — | — |
| 45 | LangChain_Agents | — | LangChain Agents info |
🔗 LangGraph & LangChain (4 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | langgraph_human_in_the_loop_HITL | Python | Langgraph with Human in the Loop using MySQL DB |
| 2 | tools_utils | Python | Langchain tools |
| 3 | Websocket_Langgraph_Integration | Python | WebSocket Langgraph Integration with user message in between like human as input |
| 4 | Langchain_pkgs_erros | — | Langchain package errors |
🔍 RAG & Search (17 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | GenAI_AWSBedrock_opensearch | Python | Gen AI utility AWS bedrock model,RAG (Opensearch) |
| 2 | reranker_utils | — | reranker utils |
| 3 | vercel_rag_builder | TypeScript | RAG application using Vercel deployment |
| 4 | docker_rags__setups | — | RAG Databases setups here Qdrant,Opensearch, Mlivus |
| 5 | AutoRAG | Python | AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation |
| 6 | ragbuilder_utils | Python | A toolkit to create optimal Production-readyRetrieval Augmented Generation(RAG) setup for your data |
| 7 | Deepseek_R1_RAG_example | Python | Deepseek_r1 model connecting with RAG |
| 8 | Verba | Python | Retrieval Augmented Generation (RAG) chatbot powered by Weaviate |
| 9 | MS_RAG_Series | — | — |
| 10 | GraphRAG_MS | Shell | — |
| 11 | email_rag_exp | HTML | T.I.M.E: Thoroughly Intelligent Mail Explorer" Repo to try and build an incredible RAG system over email (this is to test the SOTA in RAG) |
| 12 | RAG_Vector_Databases | — | — |
| 13 | R2R_RAG | — | R2R RAG Reference |
| 14 | simple-local-rag | Jupyter Notebook | Build a RAG (Retrieval Augmented Generation) pipeline from scratch and have it all run locally. |
| 15 | jobrunr | Java | An extremely easy way to perform background processing in Java. Backed by persistent storage. Open and free for commercial use. |
| 16 | practical-machine-learning-with-python | Jupyter Notebook | Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system. |
| 17 | Spark_Multi_Cloud_Storage_Utils | HTML | Spark Read/Write data from/to Multi Cloud utils (GCP, Azure and AWS) |
🏗️ Data Engineering & Pipelines (88 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | mcp_servers | Jupyter Notebook | MCP servers Collection MCP servers (Database, File system, API ,Vector databases and NOSQL ) Servers |
| 2 | Databricks-GenAI | Jupyter Notebook | — |
| 3 | data-prep-kit | HTML | Open source project for data preparation of LLM application builders |
| 4 | Databricks_LLMS | — | Databricks LLM Repo |
| 5 | apache_iceberg_hudi_deltalake_utils | Shell | iceberg |
| 6 | Data_CDC_Observability | — | — |
| 7 | helm_charts | — | helm charts for data engineering project deployments |
| 8 | SnowFlakeDBUtils_RedShiftUtils | Scala | — |
| 9 | sfguide-data-engineering-with-snowpark-python | Python | — |
| 10 | Benchmars_data | — | — |
| 11 | spark | Scala | Apache Spark - A unified analytics engine for large-scale data processing |
| 12 | lakehouse-engine | Python | The Lakehouse Engine is a configuration driven Spark framework, written in Python, serving as a scalable and distributed engine for several lakehouse algorithms, data flows and utilities for Data P… |
| 13 | databricks_data_utils | Python | — |
| 14 | databricks_lakehouse_FastAPI | — | databricks Lakehouse Fast api |
| 15 | hudi | Java | Upserts, Deletes And Incremental Processing on Big Data. |
| 16 | onetable | Java | OneTable is an omni-directional converter for table formats that facilitates interoperability across data processing systems and query engines. |
| 17 | dlt_databricks | — | — |
| 18 | dbt-experiments | — | — |
| 19 | free_datasets_Info_S3_DBFS | — | FreeDat |
| 20 | awesome-notebooks | Jupyter Notebook | A catalog of ready to use data & AI Notebook templates, organized by tools to jumpstart your projects and data products in minutes. |
| 21 | dbx_databricks_demos | — | — |
| 22 | Databricks_UnitCatalog | Jupyter Notebook | — |
| 23 | welcomedata | Python | welcomedata |
| 24 | Data-Engineer_Azure | Python | PySpark |
| 25 | Udacity-Data-Engineering-with-AWS | Jupyter Notebook | Design data models, build data warehouses, data lakes & lakehouse, automate data pipelines - SQL |
| 26 | kafka_springboot_cassandra_utils | Java | End to End Usecase Swagger UI -->Spring MicroServices -->Kafka -->Spark Consumer -->Cassandra DB |
| 27 | Hadoop_Hive_Spark_Docker_Setup | Shell | — |
| 28 | data-science-on-aws | Jupyter Notebook | AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker |
| 29 | confluent_kafka_utils | Scala | — |
| 30 | Cloud_Data_Utils | — | — |
| 31 | docker_kubernates_CICD_pipeline | Java | — |
| 32 | cheatsheets_data | — | — |
| 33 | Modern_Data_lakes | — | — |
| 34 | Cheatsheets-for-AI | — | Cheatsheets on numerous topics ranging from DataScience |
| 35 | airflow_mac_setup | — | — |
| 36 | bootcamp | Jupyter Notebook | Data Engineering and Data Science Bootcamps |
| 37 | amazon-emr-with-delta-lake | Jupyter Notebook | Amazon EMR Notebook to show how to read from and write to Delta tables with Amazon EMR |
| 38 | DataCatalog_DLP_Utils | — | — |
| 39 | AWS_GLUE | Python | — |
| 40 | Databricks_fs | — | — |
| 41 | data-engineering-zoomcamp_2022 | Jupyter Notebook | Free Data Engineering course! |
| 42 | Data-Preprocessing-Models | Jupyter Notebook | — |
| 43 | SunglassesHubETL | Python | This project is for demonstrating knowledge of Data Engineering tools and concepts and also learning in the process |
| 44 | Build-Glue-Spark-Streaming-pipeline-for-clicksstreams-and-power-data-lake-with-Apache-Hudi-and-Quer | Python | Build Glue(Spark) Streaming pipeline for clicksstreams and power data lake with Apache Hudi and Query Real time with Athena |
| 45 | ingestion-data-services | — | — |
| 46 | Getting-started-with-AWS-Glue-and-Python-Pyspark-for-Beginners | Jupyter Notebook | — |
| 47 | mlops-zoomcamp_2022 | Jupyter Notebook | Free MLOps course from DataTalks.Club |
| 48 | DataTalksClub-prv | — | — |
| 49 | pyspark-cheatsheet | Python | PySpark Cheat Sheet - example code to help you learn PySpark and develop apps faster |
| 50 | amazon-kinesis-data-analytics-examples | Java | Example applications in Java, Python and SQL for Kinesis Data Analytics, demonstrating sources, sinks, and operators. |
| 51 | pyspark-examples | Python | Pyspark RDD, DataFrame and Dataset Examples in Python language |
| 52 | DataSubmit_Databricks_2022 | — | — |
| 53 | Spark-hadoop-utils | Scala | Spark-hadoop-utils for bigdata training utils |
| 54 | spark_xml_json_utils | Scala | spark_xml_json_utils application it will read different types of xmls and jsons (simple & complex types ) write into cassandra db |
| 55 | net.jgp.labs.spark | Java | Apache Spark examples exclusively in Java |
| 56 | Content-AWS-Certified-Data-Analytics---Speciality | Jupyter Notebook | DAS-C01 ACG/LA by Brock Tubre and John Hanna |
| 57 | spark-java-microservice | Java | Spark framework is a rapid development web framework inspired by the Sinatra framework for Ruby and is built around Java 8 Lambda Expression philosophy, making it less verbose than most application… |
| 58 | Spark_java_training_utils | — | Spark_java_training_utils |
| 59 | big-data-mapreduce-course | HTML | Big Data Modeling, MapReduce, Spark, PySpark @ Santa Clara University |
| 60 | rdbms_2_nosql | Java | Course Material for "SQL, NoSQL, Big Data and Hadoop" course. |
| 61 | DataStructures-Algorithms_target | C++ | This repo contains links of resources, theory subjects content and DSA questions & their solution for interview preparation from different websites like geeksforgeeks, leetcode, etc. |
| 62 | azureeventhub_databricks_streaming_utils | — | — |
| 63 | Azure_DataFactory_utils | — | Azure_DataFactory_utils |
| 64 | Spark_Multi_Char_delimiter | Scala | Spark Multi char delimiter using RDD approach |
| 65 | azure-spark-on-kubernetes | Python | Desafio proposto pela IGTI em seu bootcamp de Cloud Data Engineer |
| 66 | spark_project_template_generator | Java | Used to generate Sample Spark Project Template |
| 67 | Livy-spark-utils | Python | A Data Engineer's Lunch demo of Apache Livy |
| 68 | timeseries_database_utils | — | time series databases |
| 69 | kafka_spark_streaming_python_end | Python | — |
| 70 | Data_Infrastructure_2022 | — | Data_Infrastructure_2022 |
| 71 | Spark_MultiFiles_Insert_Oracle | Scala | Multi Files Insert into Oracle using Spark Scala |
| 72 | Algo_Ds_Notes | C++ | It is a repository that is a collection of algorithms and data structures with implementation in various languages. |
| 73 | GCP_Data_Enginner_Utils | Shell | GCP_Data_Enginner |
| 74 | seaborn-data | Python | Data repository for seaborn examples |
| 75 | data-engineer-roadmap | — | Roadmap to becoming a data engineer in 2021 |
| 76 | Algorithms-2 | Java | A collection of algorithms and data structures |
| 77 | de-apache-spark | Python | Data Engineering com Apache Spark |
| 78 | DEPRECATED-data-structures | Java | A collection of powerful data structures |
| 79 | terraform-on-aws-ec2 | HCL | Terraform On AWS for EC2, VPC, ASG, ALB, CLB, NLB, CloudWatch, SNS, S3, CodePipeline, ACM, Route53 |
| 80 | ADPE2E | TSQL | Azure Data Platform End-to-End |
| 81 | Data-Engineering-Projects | Jupyter Notebook | Personal Data Engineering Projects |
| 82 | dataframe-examples | Scala | Apache Spark with Scala and AWS - DataFrame Examples |
| 83 | aws-athena-query-federation | Java | The Amazon Athena Query Federation SDK allows you to customize Amazon Athena with your own data sources and code. |
| 84 | PySpark_Hadoop_Postgres | Python | PySpark_Hadoop_Postgres |
| 85 | SparkCoreJDBC_Utils | Scala | — |
| 86 | docker-bigdata-utils | Jupyter Notebook | docker-bigdata-utils |
| 87 | spark-ml-deployment | Jupyter Notebook | — |
| 88 | data_2021_roadmap | — | data_2021_roadmap |
🧠 GenAI & LLM (13 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | docling_ocr_experiments | Jupyter Notebook | Docling Experiments |
| 2 | universal-copilot | Python | universal-copilot |
| 3 | open-webui-backend | JavaScript | User-friendly AI Interface (Supports Ollama, OpenAI API, ...) |
| 4 | LLM_Links | — | LLM web links info |
| 5 | chatgpt_apis_understading | — | — |
| 6 | GenAI_Utils | Python | — |
| 7 | LLM_BOOKS | — | LLM BOOKS |
| 8 | LLM_openplayground | — | openplayground |
| 9 | llm-app-stack | — | — |
| 10 | MongoDB_GenAI_Utils | — | MongoDB Gen AI Utils |
| 11 | llm-course | Jupyter Notebook | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. |
| 12 | AWS_Bedrock_AI | — | AWS Bedrock Utils |
| 13 | OpenAI | Python | — |
🛠️ MCP Servers & Tools (4 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | mcp-for-experments | Jupyter Notebook | This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed … |
| 2 | arthur-engine-with-mcp | Python | Make AI work for Everyone - Monitoring and governing for your AI/ML |
| 3 | Voice_Assistant_mcp_servers | — | VOICE assistant MCP Servers |
| 4 | mcp_remote_server | Python | MCP Remote server |
🗣️ Speech & NLP (3 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | STT_TTS_TTI_ITT_Models_experiments | Python | STT and TTS models |
| 2 | HuggingFace_NLP | — | huggingface.co |
| 3 | PerfKitBenchmarker | Python | PerfKit Benchmarker (PKB) contains a set of benchmarks to measure and compare cloud offerings. The benchmarks use default settings to reflect what most users will see. PerfKit Benchmarker is licens… |
☁️ Cloud & DevOps (42 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | sre-devops-triage-go-backend | JavaScript | — |
| 2 | azure-taskminapp | HTML | My task app in flask |
| 3 | azure-python-docs-hello-world | Python | A simple python application for docs |
| 4 | azure-cosmos-cassandra-claimprocessapi | Java | azure-cosmos-cassandra poc using claimprocessapi |
| 5 | aws_textract_utils | Python | textract utils |
| 6 | Azure_DP_100 | — | — |
| 7 | Azure_open_Ai | Python | — |
| 8 | Philly_AWS_Meetups | — | — |
| 9 | Devops-zero-to-hero | HCL | — |
| 10 | AWS_CodeWhisperer | — | — |
| 11 | SpringBoot_SpringCloud_Repo | Java | SpringBOOT_SpringCloud_repo_utils |
| 12 | AWS_weekend | — | — |
| 13 | aws-backup-prepost-script-sample | Python | — |
| 14 | docker-development-youtube-series | Go | — |
| 15 | terraform_utils | HCL | terraform_utils for IAC Infrastructure as Code |
| 16 | AWS_Serverless_Utils | — | — |
| 17 | aws_wrangler_pandas_boto3_utils | — | AWS wrangler and Pandas and S3 utils |
| 18 | terraform_aws | HCL | — |
| 19 | alibaba_cloud_utils | — | alibaba_cloud_utils |
| 20 | dms-table-mapping-generator | Python | This python script generates a JSON file for the AWS DMS table mappings section |
| 21 | Azure_Analatyics_Architecture_tools | — | — |
| 22 | Azure_synapse_repo | — | Azure_synapse_repo |
| 23 | azure_adf_utils1 | — | — |
| 24 | azure_adf_utils | — | azure_adf_utils |
| 25 | Synapse | Jupyter Notebook | Samples for Azure Synapse Analytics |
| 26 | terraform-tuesdays | HCL | Demo files for various Terraform Tuesday Examples |
| 27 | terraform-iws-main | HCL | terraform-iws-main |
| 28 | terraform-iws-modules | HCL | terraform-iws-modules |
| 29 | azure-synapse-analytics-workshop-400 | PowerShell | — |
| 30 | OCI_Oracle | — | Oracle Cloud Certifications |
| 31 | hashicorp-certified-terraform-associate-on-azure | HCL | HashiCorp Certified Terraform Associate on Azure Cloud |
| 32 | terraform-on-azure-cloud | HCL | Terraform on AWS with SRE & IaC DevOps |
| 33 | Azure-Cloud-utils | — | Azure Analytics (azure_cloud_utils) |
| 34 | azure-demo | Jupyter Notebook | — |
| 35 | python-docs-samples | Python | Code samples used on cloud.google.com |
| 36 | Implement_Security_On_AzureSynapse | TSQL | — |
| 37 | hands-on-terraform | HCL | — |
| 38 | synapseAnalytics | Jupyter Notebook | Azure Synapse Analytics Samples |
| 39 | Cloud_Utils | — | — |
| 40 | aws_kinesis_stream-utils | Python | aws_kinesis_stream_utils |
| 41 | spring-cloud-config-example | Java | — |
| 42 | Aws_Dev_Utils | Shell | — |
☕ Java & Spring Boot (2 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | anjijava16 | — | — |
| 2 | java-design-patterns | Java | Design patterns implemented in Java |
📦 Other Projects (82 repos)
| # | Project | Language | Description |
|---|---|---|---|
| 1 | cust-evals | Python | Custom Evaluations framework it is combination of arize phoneix evals ,deepval,raags frameworks |
| 2 | openclaw_moltbook_understanding | — | — |
| 3 | cedarpolicy_security | — | — |
| 4 | AI_Observability_and_Evaluation | Jupyter Notebook | AI Observability and Evaluation |
| 5 | arxiv-paper-curator | Python | — |
| 6 | redis_cache_experiments | — | Redis cache experiment's |
| 7 | autogen_contribute_0.2_vs_0.4 | Jupyter Notebook | Autogen Contribute OS |
| 8 | inside_source_packages | — | Understanding the source packages |
| 9 | github_models | Python | Github models |
| 10 | open_source_contributions_ai | — | — |
| 11 | AI-_Immersion_Holiday_Study_Group | — | AI Study Group (Virtual) - AI Immersion |
| 12 | Deep_Learning_MLOPS_Books_Series_Utils | Jupyter Notebook | — |
| 13 | ai-engineer-roadmap-2024-status | — | A roadmap describing the required skills, learning resources and sample tools to become an AI Engineer |
| 14 | AI_aiplanethub_utils | — | — |
| 15 | W-B_AI_Academy | — | — |
| 16 | CrewAI_Ref | Jupyter Notebook | — |
| 17 | deeplearning.ai | Jupyter Notebook | Notes for courses by deeplearning.ai |
| 18 | Flowise_Utils | — | Low Code Generations Gen AI |
| 19 | multipass_mac_to_linux | — | Multipass for Openserach experiments purpose |
| 20 | go_utils | Go | Go Language experiments |
| 21 | go_cobra_cli_example | Go | Go Cobra CLI examples |
| 22 | spaces-notebooks | Jupyter Notebook | Collection of notebooks for use with SingleStoreDB |
| 23 | mac_setup_utils | Shell | — |
| 24 | ML-For-Beginners_Microsoft | HTML | 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all |
| 25 | esigning | HTML | iCreditWorks offers a simple, seamless, and secure mobile loan application process. iCreditWorks utilizes this e-Signing service for it customers to sign loan agreements. |
| 26 | Canary_Analysis_netflix_-Kayenta | — | Automated Canary Analysis at Netflix with Kayenta |
| 27 | Starburst_traino_training | — | — |
| 28 | mlflow_utils | Jupyter Notebook | — |
| 29 | Tech_events | — | — |
| 30 | Myworkspace_Utils | — | — |
| 31 | evidently | Jupyter Notebook | Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b |
| 32 | Open_source_Projects_Repos | — | Open source projects repos |
| 33 | streamlit-example | Python | Example Streamlit app that you can fork to test out share.streamlit.io |
| 34 | Tech_Batch10 | — | — |
| 35 | Apache_Hudi_Workshop | — | — |
| 36 | Best_Practices | — | — |
| 37 | myweekend_work | Python | — |
| 38 | issues_fixes | — | — |
| 39 | Software_Install_md | — | — |
| 40 | fourthbrain.ai_mle-10-mkr | Jupyter Notebook | Public repository for FourthBrain.ai Machine Learning Engineering intensive |
| 41 | ml_model_serve | — | — |
| 42 | JHipster_Utils | Java | JHipster JHipster’s goal is to generate for you a complete and modern web app, |
| 43 | mlbookcamp-code_2022 | Jupyter Notebook | The code from the Machine Learning Bookcamp book and a free course based on the book |
| 44 | welcome_demo | — | This is demo project |
| 45 | ApacheFlink_Utils_Prv | Java | Apache Flink Utils |
| 46 | Prometheus | — | — |
| 47 | TechLearn_Daily | — | TechLearn_Daily |
| 48 | Books | — | — |
| 49 | fastapi_utils | Python | — |
| 50 | ApachePulsar-Utils | — | Apache Pulsar |
| 51 | greatexpectations_utils | — | — |
| 52 | Learning_ApacheDoris_opensource | — | — |
| 53 | saurabh1tna | — | Config files for my GitHub profile. |
| 54 | PythonHacks | Python | This repo contains some solved python hacker codes |
| 55 | ConcurrentExecutionUtils | Java | — |
| 56 | scripts | Jupyter Notebook | A repository of all useful scripts used in different projects/repositories in e2eSolutionsArchitect |
| 57 | apacheIgnite_utils | TSQL | different Distributed systems Apache Ignite details info |
| 58 | ultimate-fastapi-tutorial | Python | The Ultimate FastAPI Tutorial |
| 59 | Apache_kylin_Utils | — | Apache Kylin DWH |
| 60 | josephmachado | — | Profile readme |
| 61 | GraphQL_Utils | — | GraphQLDemo |
| 62 | mongo-scala-driver | Scala | — |
| 63 | encrypt_decrypt_algorithms | Java | encrypt and decrypt methods |
| 64 | Pinot_apache_utils | Dockerfile | Apache Pinot |
| 65 | Apache-Flink-Guide | — | Apache Flink Guide |
| 66 | Hackthon_2020_mp | — | Hackthon_2020_mp |
| 67 | projects_utils | — | All system projects utils and paths |
| 68 | vivsridh4 | — | My |
| 69 | free-apis-info | — | Free-APIS |
| 70 | CreditCardFraudDetectionSystem | Python | Implementation of an intelligence system to detect the fraud cases on the basis of classification. |
| 71 | github-slideshow | HTML | A robot powered training repository 🤖 |
| 72 | tech-interview-handbook | JavaScript | 💯 Materials to help you rock your next coding interview |
| 73 | sfn-crash-course | JavaScript | — |
| 74 | medium | Python | Code for medium articles https://medium.com/@niczky12 |
| 75 | Readme.md | — | — |
| 76 | daily-learn-utils | Scala | dail-learn-utils |
| 77 | MicroProfile_Utils | — | MicroProfile_Utils |
| 78 | mmtechsoft.github.io | — | — |
| 79 | song_plays_workshop_tutorial | Python | Song Plays Workshop Tutorial |
| 80 | Reactors | Jupyter Notebook | Content for Microsoft Reactor Workshops |
| 81 | iwinner-order-config-server | — | iwinner-order-config-server |
| 82 | fastapi-scheduled-ssl-checks | — | — |
- 📢 Tech Speaker — Delivered presentations at meetups on Data Engineering, AI/ML, and Cloud Technologies
- 🎯 Active Meetup Participant — Regularly attending and engaging with tech communities
- 📝 Technical Writer — Publishing articles on Medium about practical AI/ML and Data Engineering
- 🤝 Open Source Contributor — Sharing experiments, frameworks, and tools with the community
| Metric | Value |
|---|---|
| Total Repositories | 300+ |
| Certifications | 10 (AWS, Azure, GCP, Databricks, Terraform, HuggingFace) |
| Primary Languages | Python, Java, Scala, SQL |
| Categories | 10 focus areas |
"Bridging data infrastructure and intelligent AI systems to solve enterprise challenges at scale."
AI Engineer · Data Engineer · 5× Cloud Certified · 300+ Projects · Building the future with AI agents