Hi, I'm Vinod Kumar Vemula

Senior AI/Data Engineer

Transforming data into intelligent solutions with 7 years of expertise in GenAI, RAG systems, LLM fine-tuning, and scalable cloud platforms across Azure, AWS, and GCP.

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Dallas, TX 75071
7+ Years Experience
Available for Projects

About Me

Professional Summary

Senior AI/Data Engineer with 7 years of experience building production-grade GenAI systems and scalable data platforms.

I specialize in architecting and deploying cutting-edge AI solutions, including RAG (Retrieval-Augmented Generation) systems, multi-agent architectures, and LLM fine-tuning. My expertise spans across major cloud platforms—Azure, AWS, and GCP—where I've delivered solutions processing 10M+ daily events.

Throughout my career, I've consistently delivered impactful results: reducing operational costs by 20-30%, accelerating deployment cycles, and implementing innovative solutions in finance and IoT sectors.

GenAI Expertise

RAG systems, multi-agent architectures, and LLM fine-tuning

Cloud Platforms

Azure, AWS, and GCP with production-scale deployments

Proven Impact

10M+ daily events, 20-30% cost reduction

Experience

Professional Journey

AI Engineer

CGI, Dallas, TX Oct 2025 – Present
  • Architected and deployed production-grade GenAI microservices using FastAPI, Flask, Docker, and Kubernetes; delivered end-to-end solutions leveraging OpenAI GPT, Anthropic Claude, and Google Gemini models with advanced prompt engineering and optimal model selection
  • Fine-tuned large language models using Hugging Face Transformers, LoRA, QLoRA, PEFT, and Unsloth, achieving improved domain specialization, reduced inference costs, and higher performance on targeted tasks
  • Designed and implemented multi-agent systems with LangGraph, AutoGen, and CrewAI to enable structured collaboration and automate complex workflows; enhanced knowledge-driven reasoning by integrating agents with Neo4j knowledge graphs, REST APIs, and real-time data connectors for entity-aware inference
  • Engineered advanced Retrieval-Augmented Generation (RAG) pipelines using LangChain, LlamaIndex, FAISS, Pinecone, and Weaviate, significantly improving retrieval accuracy and grounding LLM responses in reliable semantic search
  • Built memory-augmented agent systems with persistent long-term memory stores and moderation frameworks, ensuring contextual continuity, reasoning alignment, and reduced hallucinations in production deployments
  • Accelerated data ingestion and preparation for LLM training/fine-tuning using PySpark, Apache Airflow, and Azure Data Factory, processing large-scale datasets efficiently
  • Developed comprehensive evaluation frameworks with RAGAS, LangChain evaluators, and reinforcement learning-based scoring to quantify and optimize key LLM metrics including coherence, relevance, factual accuracy, and hallucination rates
GenAI RAG LangChain Multi-Agent LangGraph FastAPI LoRA Kubernetes

Data Engineer

BMO Bank, Chicago, IL Jun 2023 – Sept 2025
  • Developed end-to-end GenAI applications (GPT, Claude, Gemini) and architected Retrieval-Augmented Generation (RAG) pipelines (LangChain, FAISS) to enhance semantic search and contextual grounding
  • Built agentic and multi-agent AI systems (LangGraph, AutoGen) and integrated memory-augmented LLM systems with safety-aware reasoning for reliable and consistent outputs
  • Designed data models in Azure Synapse and SQL Server for regulatory compliance and financial reporting for 50+ stakeholders
  • Implemented CI/CD pipelines with Azure DevOps and ARM templates, accelerating deployment cycles by 30%
  • Built Power BI dashboards from Synapse data, delivering insights to management on $1B+ portfolios
  • Streamlined data ingestion with Azure Kubernetes Service and Docker, improving scalability for 10M+ daily transactions
GenAI RAG Azure Synapse Power BI Azure DevOps Kubernetes LangGraph FAISS

Data Engineer – Intern

John Deere, Moline, IL Aug 2022 – Jan 2023
  • Built and optimized cloud-based data pipelines for large-scale IoT sensor data using AWS services including S3, Lambda, and Redshift, supporting analytics across 10,000+ connected machines
  • Automated infrastructure provisioning using Terraform and CloudFormation, reducing environment setup time by ~25%
  • Developed serverless workflows to process real-time telemetry data, improving operational visibility and lowering infrastructure costs by ~20%
  • Collaborated with senior data engineers and platform teams to monitor pipeline health using CloudWatch and Grafana, contributing to 99.8% system uptime
AWS IoT S3 Lambda Redshift Terraform CloudFormation Serverless

Sr. Technology Support

Infosys, India Mar 2019 – Dec 2021
  • Automated CI/CD workflows with Cloud Composer, reducing deployment errors by 15%
  • Utilized Python and SQL for data transformation, supporting sales analytics for 50+ products
  • Implemented Cloud Pub/Sub and Kafka for real-time analytics, processing 500K+ daily transactions
  • Leveraged Cloud Natural Language API to analyze customer sentiment, improving product satisfaction by 10%
  • Monitored and troubleshot cloud-based data pipelines and messaging systems, performing root-cause analysis to resolve production issues and improve system stability and uptime
Cloud Composer CI/CD Python SQL Pub/Sub Kafka Cloud NLP

Data Engineer

Aviva Life Insurance, Bangalore, India Jan 2018 – Feb 2019
  • Developed BigQuery ETL pipelines for 500K+ insurance policies with 99.9% uptime
  • Created Power BI dashboards integrated with BigQuery, enhancing sales tracking for 200+ agents
  • Integrated AWS S3 and Redshift with GCP for multi-cloud processing, reducing data transfer costs by 15%
  • Optimized SQL queries and BigQuery job configurations to improve query performance and reduce processing time and operational costs
  • Used Sqoop and Impala for high-speed analytics on claims data, supporting regulatory reporting for 100K+ policies
  • Implemented data quality checks, validation rules, and scheduled monitoring for ETL pipelines to ensure accuracy, consistency, and compliance in insurance reporting systems
BigQuery Power BI AWS GCP SQL Sqoop Impala

Skills & Technologies

Technical Expertise

Visualization

Power BI Tableau AWS Quicksight

GenAI & LLMs

OpenAI GPT Claude Gemini Hugging Face LoRA/PEFT Unsloth Prompt Engineering

Retrieval, RAG & Multi-Agent

RAG LangChain LlamaIndex LangGraph AutoGen CrewAI RAGAS FAISS Pinecone Weaviate

ML / Deep Learning

PyTorch TensorFlow Scikit-learn XGBoost LightGBM CNNs Optuna

Cloud Platforms

Azure (ADF, Synapse, Databricks) AWS (S3, Redshift, Glue, Lambda) GCP (BigQuery, Dataflow, Pub/Sub)

Big Data

Hadoop Spark/PySpark Hive Kafka Kinesis

Programming

Python SQL Scala Java

Tools & Infrastructure

Airflow Terraform Jenkins Docker Kubernetes

Databases

PostgreSQL MySQL MongoDB Snowflake DynamoDB

Domain Experience

Finance IoT Enterprise AI Real-time Processing Cost Optimization

Education

Academic Background

Masters in Statistics and Decision Analytics

Western Illinois University, USA

GPA: 3.60

Bachelor of Technology in Mechanical Engineering

India

GPA: 3.00

Featured Projects

Notable Work & Achievements

Production GenAI Microservices

Architected and deployed scalable GenAI microservices using FastAPI and Kubernetes, integrating multiple LLM providers (OpenAI, Claude, Gemini) for enterprise applications.

GenAI FastAPI Kubernetes LLM

RAG System Implementation

Built sophisticated Retrieval-Augmented Generation systems for enterprise knowledge management, improving accuracy and reducing hallucinations in AI-generated responses.

RAG Vector DB NLP LLM

Multi-Agent Architecture

Designed and implemented multi-agent systems for complex workflow automation, enabling autonomous decision-making and task delegation across distributed systems.

Multi-Agent AI Automation Python

Scalable Data Platform

Built cloud-native data platforms processing 10M+ daily events with 20-30% cost reduction, serving finance and IoT sectors with real-time analytics capabilities.

Azure Big Data Real-time ETL

LLM Fine-tuning Pipeline

Developed automated pipelines for fine-tuning large language models on domain-specific data, achieving superior performance for specialized enterprise use cases.

LLM Fine-tuning MLOps PyTorch

Get In Touch

Let's Collaborate

Location

Dallas, TX 75071

Phone

(279) 500-3888

Email

vvinodvemula03@gmail.com

LinkedIn

linkedin.com/in/vinod-kumar-vemula007/