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AI & Agentic Transformation Architect | Enterprise AI Leader | MLOps & LLM Systems
Technology leader with 11+ years of experience architecting enterprise AI platforms and driving large-scale AI transformation. Currently leading Agentic AI strategy at PepsiCo's Global IT Operations in Hyderabad and deploying AI agents that autonomously remediate ServiceNow incidents and requests, reducing human intervention, mean time to resolution, and operational costs at scale. Proven track record of translating complex AI capabilities into production systems with measurable business impact. Bridges deep technical architecture with cross-functional leadership, enterprise governance, and strategic stakeholder alignment.
International Institute of Information Technology Hyderabad (IIITH)
Master's Degree, Data science
January 1, 2013 – January 1, 2015
Bharat Institute of Engineering and Technology.
Bachelor's Degree, Computer Science
January 1, 2008 – January 1, 2012
Bhashyam Public School
High School
January 1, 2002 – January 1, 2006
PepsiCo India
Architect - AI & Agentic Transformation (IT Ops)
August 1, 2025 – Present
Hyderabad, Telangana, India · Hybrid
IGT
Machine Learning Engineer IV
July 1, 2021 – August 1, 2025
Hyderabad, Telangana, India · Hybrid
SS&C Technologies
Data Engineer
February 1, 2020 – July 1, 2021
Hyderabad, Telangana, India
Innominds
Machine learning Engineer - Big Data & Analytics
June 1, 2015 – January 1, 2020
Innominds
Internship Trainee - Big Data & Analytics
January 1, 2015 – June 1, 2015
IIITH
Graduate Teaching Assistant
June 1, 2014 – July 1, 2014
Hyderabad Area, India
TARA MOBILES TECHNOLOGIES
Summer Intern
March 1, 2013 – June 1, 2014
Greater Hyderabad Area
Client Services
January 1, 2019 – Present
Project summary: We implements the mobile data requests which include the audience, walk-in ’s, impressions and trend of the advertisement results in adtech domain. We provide an optimized way to pull the data based on the data and designing a pipeline to automate the data requests. Technologies: Scala, Python 2.7+, Apache Spark 2.2, Databricks (AWS)
Royality management (Client: PwC)
June 1, 2018 – August 1, 2018
Technologies: IBM Hyperledger Fabric, NodeJS
Vistex 2.0
April 1, 2018 – December 1, 2018
This version of Vistex concentrates on storing the large agreements and extracted contract information using the OCR capabilities. Features included updating the existing the contract and accommodate contractual information shareable to multiple receivers. Technologies: IBM Hyper ledger Fabric, NodeJS
Sheltered Harbor
January 1, 2018 – Present
We have implemented "Sheltered Harbor Monitoring" using ethereum Blockchain. The Monitoring Log enables the Sheltered Harbor Community to monitor participants compliance with the daily archiving of critical account data. The Monitoring Log utility, managed by Sheltered Harbor, accepts and records Daily Attestation Messages. Technologies: Ethereum, Quorum 2.0, NodeJS, AngularJS.
Cognitive Automation Platform (Client: Deloitte)
January 1, 2018 – January 1, 2019
We implemented a solution that leverage analytics algorithms and result in agent productivity improvement in terms of low time to resolution, access to the exhaustive knowledge repository. Technologies: Python 2.7+, Apache Hadoop, Apache Spark 2.2, Tableau
Real time face detection using webcam
January 1, 2018 – February 1, 2018
Technologies used: Opencv2
Sales Analytics solution for International part sales (IPS) of Fuso trucks. (Client: Daimler)
November 1, 2017 – December 1, 2017
Objective of the project is to provide an analytics solution for international sales data of parts which provide insights on the parts sales, supply chain etc. Also provide a demand forecast of spare parts for heavy weight and light weight trucks in various stages of supply chain. Technologies : Spark, Scala, Tableau.
Energy Orientation (Client : Robert Bosch)
October 1, 2017 – April 1, 2018
Knowing the source of energy is critical for clean & affordable energy. Today’s infrastructure is incapable of providing a trustworthy source of origin for the “energy supplier.” This project aims to achieve a tamper proof and real time record of the energy produced, supplied and consumed in the form of Certificate of Origins lifecycle. Technologies: Ethereum, IBM Hyperledger Fabric, NodeJS
Blockchain-based agreement sharing platform (Client: Vistex)
April 1, 2017 – October 1, 2017
This solution helps clients to have secure and transparent transactions over the network. It helps to enable digital smart contracts by eliminating the physical agreements. Technologies: Blockchain, web3js, solidity, Ethereum, quorum.
ConvergeHEALTH Cognitive Intake (Client: Deloitte)
December 1, 2016 – April 1, 2017
Pharmacovigilance (PV) organizations face a growing volume of adverse event (AE) cases, but today’s manual processes can be time-consuming and costly. We designed a way to automate AE processing to help reduce costs and uncover more insights that can improve product safety. We have built an intelligent system which will use the cognitive analytics for Recommendations and Signal Detection. Technologies: Python, Spark, RabbitMQ, MongoDB, Docker
Identity verification using Smart Contracts and Distributed Ledger Technology. (POC)
August 1, 2016 – December 1, 2016
Government agencies in different states issue identity and other documents to their citizen. Sometimes they are digitally verifiable, other times they are not. Also an individual has multiple documents proving her identity and certifications and are not easily retrievable. The physically documents may get lost or damaged. The documents are usually only verifiable only within the border of the state. Outside the borders only passports are considered valid identity. And if the user doesn’t have passport with her, or for some reason can’t carry one (e.g. refugees), her identity cannot be established. The refugees cannot get a job since they cannot prove their educational qualifications. Due to natural disasters (e.g. Haiti1), it is possible that the government servers get damaged and identities and documents can no longer be verified. We will consider and integrate with one such unique identity program. Technologies: Ethereum, Solidity, web3js, quorum
Viridis 2.0
December 1, 2015 – August 1, 2016
Viridis is a SaaS platform that matches skill deficiencies with local employer needs of middle skill talent. The company is located in US and has a team distributed across US, Argentina and India • Working in an agile team creating new features and maintaining the existent ones in the viridis platform. • Frontend development using Backbone (Marionette), RequireJS, Grunt, Bower, LESS and Bootstrap • Restful API development using Node.js (Express), MongoDB and Elasticsearch • Recommendations using Python (Pandas, SciKit) • Methodologies: SCRUM • Version Control: GIT • Toolset: Sublime Text, Robomongo, SourceTree, Github, JIRA
Tetra Product (Client: Deloitte)
February 1, 2015 – August 1, 2015
"Tetra" is a UI framework for standardizing the navigation/look/feel of Deloitte analytics products and ensuring that analytics products do not look like “just another Tableau/Qlikview document” Tetra wraps BI visualizations and filters within an HTML5/AngularJS framework and also enables the use of BI visualizations and components within non-BI applications Technologies: Tableau Desktop and Server 8.2.
Polaris Product (Client: Deloitte)
January 1, 2015 – February 1, 2016
Polaris is a Managed Analytic solution to help companies drive profitable growth. Polaris leverages to provide industry-specific, cross-system decision workflows and dashboards, with a focus on specificity, granularity.
Predictive analytics tools evaluation
January 1, 2015 – August 1, 2015
This analysis is done as groundwork for a product, "Iscope". As a part of it our team analysed ~10 predictive tools. I analysed 3 tools - Revolution R analytics, IBM SPSS and Predixion. We analysed performance & accuracy of machine learning algorithms like clustering, classification, regression with each tool. Various measures like accuracy, confusion matrix, gini coefficient & root mean square error are considered in this analysis. Scalability and user friendliness of each tool is observed w.r.t other tools.
Forecasting and time series analysis - Manufacturing case study
January 1, 2015 – February 1, 2015
We developed an ARIMA model to forecast car sales/demand for next year. Additionally, we also investigated the impact of marketing programon sales by using an exogenous variable ARIMA model. Our model includes business time series analysis based on components like Trend,Seasonality,Cycle,Irregular remainder. Visualization of Auto Correlation factor (ACF) and partialautocorrelationfactor(PACF)plotsareusedtoidentifypatternsusingggplotpackage.
S&P Stock Portfolio
September 1, 2014 – Present
Analyse and forecast the share value of all S&P companies based on historic data using time series models.
Design, Develop, and Deploy Multi-Agent Systems with CrewAI
DeepLearning.AI
June 24, 2026 – Present
Deep Learning Specialisation Certificate
DeepLearning.AI
June 24, 2026 – Present
Sequence Mode
DeepLearning.AI
June 24, 2026 – Present
Convolutional Neural Networks
DeepLearning.AI
June 24, 2026 – Present
Structuring Machine Learning Projects
DeepLearning.AI
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
DeepLearning.AI
June 24, 2026 – Present
Neural Networks and Deep Learning
DeepLearning.AI
June 24, 2026 – Present
Computer Vision with OpenCV Library using Python
Udemy
June 24, 2026 – Present
Spark and Python for Big Data with PySpark
Udemy
June 24, 2026 – Present
IBM Blockchain Foundation for Developers
IBM
June 24, 2026 – Present
ORACLE CERTIFIED JAVA PROGRAMMER
ORACLE
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project history shows a diverse range of applications, from real-time face detection and blockchain to sales analytics and pharmacovigilance. This breadth indicates adaptability and a willingness to tackle varied challenges. The experience with Deloitte, Daimler, and PwC suggests exposure to corporate environments and client-facing roles. However, the target role is 'Data Analyst', while the candidate's recent experience is heavily skewed towards 'Machine Learning Engineer' and 'Architect - AI'. This might indicate a potential mismatch in the desired role responsibilities, as a Data Analyst role typically focuses more on data extraction, transformation, visualization, and reporting, rather than ML model development and AI architecture. While the candidate possesses strong analytical skills, the recent career trajectory suggests a more advanced, engineering-focused role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate experience in agile environments (SCRUM) and working with distributed teams. The roles at PepsiCo and IGT suggest leadership in defining standards and architecting solutions, which implies strong problem-solving and collaboration skills. However, without direct assessment data, specific soft skills and operational fit cannot be definitively evaluated.