---
title: "Dattatray Dubile - Senior Agentic AI Engineer in Mumbai | Tal"
description: "Meet Dattatray Dubile, Agentic AI Engineer | Senior Data Engineer | Capgemini | Python | Pyspark | SQL | Microservices | LLM | RAG | Langchain | LangGraph | Agentic AI | MCP | A2A based in Mumbai. AI stack includes aistudio, langfuse, and sora."
image: "https://tal.club/meet/dattatray-dubile/share-image"
---

# Dattatray Dubile on Tal

## Headline

Agentic AI Engineer \| Senior Data Engineer \| Capgemini \| Python \| Pyspark \| SQL \| Microservices \| LLM \| RAG \| Langchain \| LangGraph \| Agentic AI \| MCP \| A2A

## About

Agentic AI Engineer and Data Engineering at Capgemini, specializing in production\-grade Generative AI systems\. I design and implement RAG and non\-RAG LLM pipelines, LangGraph\-based multi\-turn agent workflows, MCP\-integrated tool orchestration, and knowledge\-driven agents powered by vector databases and Neo4j knowledge graphs for hybrid \(Vector \+ Graph\) retrieval systems\. My experience includes building LLM evaluation and validation frameworks covering relevance, correctness, consistency, hallucination detection, and edge\-case analysis—improving response precision and reducing downstream errors by \~25% while enhancing system reliability to 96%\+ through monitoring, retry logic, and exception handling\. I apply system\-level design principles to agentic workflows, focusing on memory management, prompt versioning, retrieval optimization, turn tracking, and response evaluation\. Beyond AI systems, I architect event\-based and schedule\-based orchestration workflows using AWS Step Functions and cloud\-native services, design secure microservices with JWT authentication, RBAC authorization, and session management, and develop end\-to\-end Bronze–Silver–Gold lakehouse pipelines using Python, PySpark, Snowflake, AWS, and Azure\. I also build OCR\-driven unstructured data processing pipelines integrated into intelligent retrieval systems\. Strong in Python, distributed data engineering, cloud platforms, and LLM foundations, I focus on building reliable, scalable, and measurable AI\-data systems that deliver real\-world business impact in Agile, cross\-functional environments\.

## Location

Mumbai

## AI stack

aistudio

langfuse

sora

v0

## Work experience

Senior Agentic AI Engineer at Capgemini, Apr 2023 \- Present · 3 yrs 2 mos, Mumbai, Project: IDEA SDLC \(Agentic AI\-IDEA\) • Architected and deployed a multi\-agent AI platform using AWS Bedrock \(LLM\) and LangGraph, enabling end\-to\-end SDLC automation through intelligent, multi\-turn agent orchestration\. • Designed specialized agents for automated code generation \(multi\-language support\), BRD documentation, HLD generation, data mapping design, and synthetic data creation, accelerating development lifecycle efficiency by \~45%\. • Implemented self\-healing code agents with automated debugging, optimization, and standardization workflows, reducing manual rework by \~35% and improving code quality compliance\. • Built hybrid RAG and non\-RAG pipelines integrating vector embeddings and Neo4j knowledge graphs \(Graph \+ Vector Retrieval\), improving contextual accuracy by \~30%\. • Developed memory\-aware agent workflows with session persistence, conversation tracking, and long\-term knowledge retention to enhance multi\-turn reasoning consistency\. • Engineered LLM evaluation frameworks covering relevance, correctness, hallucination detection, and edge\-case validation—improving response precision by \~25% and achieving system reliability of 96%\+\. • Built embedding pipelines and semantic search layers using vector databases and Neo4j knowledge bases to support knowledge\-driven chatbot interactions\. • Integrated monitoring, retry logic, and automated guardrail enforcement to ensure safe, consistent, and production\-grade LLM outputs\. • Delivered an intelligent chatbot platform combining LLM reasoning, knowledge graph traversal, RAG retrieval, and agent collaboration to enable scalable AI\-driven SDLC transformation\.

Data Analyst at Capgemini, Apr 2023 \- Present · 3 yrs 2 mos, Mumbai, Maharashtra, India, Project: Sustainability Data Hub\(Azure\-SDH\) • Developed parameterized ADF pipelines for batch ingestion of data files from ADLS, utilizing a • • microservice\-based ingestion framework\. • Built ADF pipelines for stream ingestion using Kafka Confluent, enabling real\-time Datasphere data migration from APIs to ADLS\. • Implemented ADF pipelines for API\-based ingestion of SAP Datasphere data into ADLS\. • Designed and implemented ADF pipelines for data migration, coupled with Azure Databricks notebooks to perform transformations on ingested data\. • Performed layered data processing \(Bronze → Silver → Gold\), incorporating cleansing, reconciliation, and profiling operations across each layer\. • Stored all job\-related metadata into MongoDB, capturing job registration and execution details\. • Used Mobaxterm to monitor backend services, check pod statuses, and analyze logs\.\. • Stored Silver layer transformed data into PostgreSQL tables, and performed further transformation at the Gold layer, saving the final outputs into Gold tables in PostgreSQL\. • Developed end\-to\-end microservices using Python and Flask to support metadata extraction from diverse sources and transformed it into visual formats for data analysis\.

Data Analyst at Capgemini, Apr 2023 \- Present · 3 yrs 2 mos, Mumbai, Maharashtra, India, Project: Sustainability Data Hud \(AWS\-SDH\) • Developed microservices configuring SDH with source/target/intermediate file store connection details\. • Developed Teradata\-Redshift Stream microservices in SDH for schema conversion and object creation on AWS Redshift using regex\. • Developed Teradata\-Redshift microservice using Glue PySpark Script for diverse data loading \(Full, Incremental, Adhoc\) from source to staging and from staging to destination\. • Developed Amazon Glue PySpark Script for diverse source data batch ingestion to S3 Bucket, enabling Full and Incremental Load operations\. • Developed parameterized PySpark notebook templates on EMR for data transformation on ingested data\. • Utilized SonarQube to analyze and mitigate code issues, achieving an 80% reduction in bugs and noise through code refactoring\. • Managed code versioning with bit bucket and deployment to staging and production servers\. • Utilize PyTesting, the Python unit test framework, for all Python applications\.

Internship Trainee at Metalman Auto Limited, Jun 2021 \- Sep 2021 · 4 mos, Aurangabad, Maharashtra, India, • Understand Requirements: Gather all necessary specifications and requirements for the sheet metal designs\. • Draft Initial Designs: Use AutoCAD to draft the initial designs, ensuring they align with the sheet metal die specifications\. • Detailing: Add detailed dimensions, tolerances, and annotations to the designs\. • Review and Revise: Review the designs for accuracy and make necessary revisions based on feedback\. • Import 2D Designs: Import the 2D designs created in AutoCAD into SolidWorks\. • Create 3D Models: Use SolidWorks to convert the 2D designs into 3D models, ensuring all features and specifications are accurately represented\. • Simulation and Analysis: Perform simulations and analyses to check the feasibility and performance of the 3D models\. • Finalize Designs: Make any necessary adjustments and finalize the 3D designs for production\.

## Education

Bachelor of Engineering \- BE, Mechanical Engineering, Sanjivani College of Engineering kopargaon

Bachelor of Engineering \- BE, Mechanical Engineering, Savitribai Phule Pune University

[View the original profile](https://tal.club/meet/dattatray-dubile)

```json
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Person",
      "@id": "https://tal.club/meet/dattatray-dubile#person",
      "name": "Dattatray Dubile",
      "url": "https://tal.club/meet/dattatray-dubile",
      "image": "https://tal.club/meet/dattatray-dubile/profile-photo.jpg",
      "description": "Meet Dattatray Dubile, Agentic AI Engineer | Senior Data Engineer | Capgemini | Python | Pyspark | SQL | Microservices | LLM | RAG | Langchain | LangGraph | Agentic AI | MCP | A2A based in Mumbai. AI stack includes aistudio, langfuse, and sora.",
      "jobTitle": "Senior Agentic AI Engineer",
      "worksFor": {
        "@type": "Organization",
        "name": "Capgemini"
      },
      "hasOccupation": {
        "@type": "Occupation",
        "name": "Senior Agentic AI Engineer"
      },
      "homeLocation": {
        "@type": "Place",
        "name": "Mumbai"
      },
      "alumniOf": [
        {
          "@type": "EducationalOrganization",
          "name": "Sanjivani College of Engineering kopargaon"
        },
        {
          "@type": "EducationalOrganization",
          "name": "Savitribai Phule Pune University"
        }
      ],
      "knowsAbout": [
        "aistudio",
        "langfuse",
        "sora",
        "v0"
      ],
      "mainEntityOfPage": {
        "@id": "https://tal.club/meet/dattatray-dubile#webpage"
      }
    },
    {
      "@type": "ProfilePage",
      "@id": "https://tal.club/meet/dattatray-dubile#webpage",
      "url": "https://tal.club/meet/dattatray-dubile",
      "name": "Dattatray Dubile on Tal",
      "description": "Meet Dattatray Dubile, Agentic AI Engineer | Senior Data Engineer | Capgemini | Python | Pyspark | SQL | Microservices | LLM | RAG | Langchain | LangGraph | Agentic AI | MCP | A2A based in Mumbai. AI stack includes aistudio, langfuse, and sora.",
      "dateModified": "2026-06-10T13:55:59.456Z",
      "isPartOf": {
        "@id": "https://tal.club/#website"
      },
      "mainEntity": {
        "@id": "https://tal.club/meet/dattatray-dubile#person"
      },
      "primaryImageOfPage": {
        "@type": "ImageObject",
        "url": "https://tal.club/meet/dattatray-dubile/profile-photo.jpg",
        "contentUrl": "https://tal.club/meet/dattatray-dubile/profile-photo.jpg",
        "caption": "Dattatray Dubile profile photo on Tal"
      },
      "inLanguage": "en-IN"
    }
  ]
}
```