Ganesh Bandgar - AI Agent Designer | ContraWork by Ganesh Bandgar
Ganesh Bandgar

Ganesh Bandgar

Data Engineer, Data annotation, AI Engineer, AWS, SQL, .py

New to Contra

Ganesh is ready for their next project!

Followed by Moch Virgiawan C and GALLERY L

Solapur, India

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Solapur, India

Cover image for Data Product Builder Agent Technology &
Data Product Builder Agent Technology & Tools: Generative AI, Agentic AI, AI Agents, Multi-Agent Systems, LLM, RAG, MCP, Vector Databases, Embeddings, Semantic Search, Hybrid Search, LangChain, LangGraph, CrewAI, AutoGen, OpenAI, Claude, AWS Bedrock, Cursor, Python, FastAPI, REST APIs, Docker, Git, GitHub, AWS, S3, Lambda, OpenSearch, PostgreSQL, CI/CD, Prompt Engineering, Context Engineering, Tool Calling, Function Calling, Structured Outputs, Human-in-the-Loop, AI Guardrails, LLMOps Role: Data Engineer / AI Engineer • Designed an enterprise-grade AI-powered Data Product Builder Agent to accelerate creation, validation, documentation, governance, and management of reusable data products. • Implemented Agentic AI and Multi-Agent Systems with specialized agents for metadata discovery, schema analysis, transformation, validation, documentation, orchestration, and deployment. • Built stateful LLM workflows using LangChain and LangGraph for reasoning, tool calling, structured outputs, agent memory, error handling, and Human-in-the-Loop approvals. • Implemented RAG using embeddings, Vector Databases, semantic search, hybrid search, and knowledge bases to ground LLM responses in enterprise data and metadata. • Integrated MCP for standardized context and tool integration and used CrewAI and AutoGen patterns for multi-agent collaboration and task delegation. • Integrated OpenAI, Claude, and AWS Bedrock for model flexibility, prompt/context engineering, evaluation, guardrails, and LLMOps. • Developed Python and FastAPI REST APIs and integrated S3, Lambda, OpenSearch, and PostgreSQL for data, metadata, retrieval, and application workflows. • Used Cursor, Git, GitHub, Docker, and CI/CD for AI-assisted development, version control, containerization, testing, and deployment
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Cover image for Publishing Price Scraping Data Platform Technology
Publishing Price Scraping Data Platform Technology & Tools: Python, AWS Glue, Amazon S3, Athena, CloudFormation, Apache Spark, Spark SQL, Azure, Azure Data Factory, ADLS Gen2, Azure Databricks, Azure Synapse Analytics, Airflow, Amazon Redshift, PySpark, MySQL Role: Data Engineer • Developed ETL processes using AWS Glue, PySpark, Apache Spark, and Spark SQL for large-scale publishing and product data processing. • Managed Data Lake storage in S3 and analytical workloads in Athena and Redshift, ensuring data quality and consistency across pipeline stages. • Implemented orchestration using Apache Airflow and cloud-native services and optimized transformations for distributed data processing. • Worked with MySQL and performed statistical analysis on inventory and product data. • Used Azure Data Factory, ADLS Gen2, Azure Databricks, and Azure Synapse Analytics for ingestion, storage, processing, transformation, and analytics.
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Cover image for AWS Cost Optimization Technology & Tools:
AWS Cost Optimization Technology & Tools: Python, AWS Glue, Amazon Redshift, Snowflake, Amazon S3, Athena, CloudFormation, PySpark, Spark SQL, CloudWatch, AWS Cost Optimization Role: Data Engineer • Built pipelines to process and analyze AWS usage and cost data using Glue, PySpark, S3, Athena, Redshift, and Snowflake. • Designed Data Lake and Data Warehouse workflows for cost visibility, reporting, performance analysis, and cloud cost optimization. • Applied Redshift optimization concepts including query tuning, distribution and sort strategies, workload management, storage optimization, and Serverless vs. provisioned workload considerations. • Implemented scalable partitioning and query patterns and supported operational monitoring using Amazon CloudWatch. • Used AWS CloudFormation and GitHub Actions for repeatable infrastructure provisioning and CI/CD-based deployments.
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Cover image for Enterprise Data Platform Technology & Tools:
Enterprise Data Platform Technology & Tools: Python, Java, SQL, PySpark, Apache Spark, Spark SQL, AWS, Amazon S3, AWS Glue, PostgreSQL, Snowflake, BI, Terraform, ETL, ELT, Data Lake, Data Warehouse, Data Lakehouse Role: Data Engineer • Designed scalable end-to-end ETL/ELT pipelines and batch data processing workflows using Python, PySpark, Spark SQL, AWS, and Snowflake. • Defined ingestion, transformation, validation, data flow, and data modeling patterns for an enterprise Data Lake, Data Warehouse, and Data Lakehouse platform. • Reviewed Spark execution plans and optimized transformations, joins, aggregations, partitioning, and processing performance for distributed workloads. • Used Terraform for Infrastructure-as-Code and collaborated across engineering, product, and business teams to deliver scalable data solutions. • Wrote and optimized Advanced SQL queries and supported data quality, reliability, consistency, metadata, and governance requirements.
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