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CTPO @Geoserve|Product & Engineering | Maritime Tech| E-commerce | AI| Consumer Internet
Expert in architecting innovative solutions across diverse industries — including maritime, e-commerce, supply chain, healthcare, ride-sharing, logistics, and unified communications. A passionate leader of cross-functional teams — product, engineering, design, data, and dev-ops — with a strong focus on fostering innovation, driving business value, and delivering scalable solutions #CTPO #TechnologyLeadership #Product#Architecture #Innovation#AI#CultureBuilding#GenerativeAI
Indira Gandhi National Open University
PGDIM, Management and Finance
April 1, 2004 – March 1, 2006
University of Allahabad
MSc, Computer Science
January 1, 1996 – January 1, 1998
GeoServe
Chief Technology and Product Officer
June 1, 2024 – Present
Dubai, United Arab Emirates · On-site
Stealth Startup
Co-Founder
October 1, 2023 – June 1, 2024
Greater Bengaluru Area · On-site
Inflection Point Ventures
Investor
June 1, 2023 – June 1, 2024
Bangalore
Multiple companies
Product/Tech Advisory Role
May 1, 2023 – June 1, 2024
Bangalore Urban, Karnataka, India · Hybrid
Career Break
Personal goal pursuit
November 1, 2022 – April 1, 2023
Greater Bengaluru Area
DFI Retail Group
Head Of Product Development
March 1, 2022 – October 1, 2022
Singapore
Homage
Chief Technology Officer
July 1, 2021 – March 1, 2022
Singapore
Lazada
Executive Vice President Technology (earlier many roles in Redmart)
April 1, 2017 – June 1, 2021
Singapore · On-site
Ola (ANI Technologies Pvt Ltd)
Senior Director of Engineering
January 1, 2016 – February 1, 2017
Bangalore
Jabong
Director of Engineering
June 1, 2014 – December 1, 2015
Bangalore
Flipkart.com
Senior Engineering Manager
January 1, 2013 – May 1, 2014
Bangalore
Actiance, Inc.
Senior Engineering Manager
March 1, 2007 – January 1, 2013
Bangalore, India
Hewlett-Packard
Software Analyst
July 1, 2003 – March 1, 2007
Bangalore
Induslogic S/W P Ltd
Technical Leader
March 1, 2003 – July 1, 2003
Greater Delhi Area
Aztec Software Services Ltd
Technical Leader
July 1, 1999 – February 1, 2003
Bangalore
Nucleus Software
Software Engineer
February 1, 1998 – June 1, 1999
Top 10 Skills for Machine Learning Cloud Architects
June 25, 2026 – Present
Digital Marketing Foundations
June 25, 2026 – Present
Python in Excel
June 25, 2026 – Present
How to Research and Write Using Generative AI Tools
June 25, 2026 – Present
Generative AI for Business Leaders
June 25, 2026 – Present
Google Analytics Quick Start
June 25, 2026 – Present
TensorFlow: Neural Networks and Working with Tables
June 25, 2026 – Present
Generative AI with Large Language Models
Coursera
June 25, 2026 – Present
LinkedIn AI Academy AI-100: 1 Demystifying AI
June 25, 2026 – Present
GPT-4: The New GPT Release and What You Need to Know
June 25, 2026 – Present
Prompt Engineering: How to Talk to the AIs
June 25, 2026 – Present
Generative AI: Working with Large Language Models
June 25, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse career path spanning various industries (e-commerce, ride-sharing, maritime, compliance) and roles from individual contributor to C-level. This breadth suggests adaptability and a willingness to tackle different challenges. The recent certifications in AI/ML topics indicate a proactive approach to learning and aligning with current technological trends, which is a positive cultural fit for an evolving tech environment. However, the primary experience is in leadership and product management, not direct ML engineering, which might require a cultural shift if the target role is a hands-on ML Engineer.
Soft Skills & Operational Fit
The candidate's extensive experience in leadership roles (CTO, Head of Product Development, Senior Director of Engineering) indicates strong soft skills in stakeholder management, team leadership, and strategic planning. The descriptions highlight a focus on driving business outcomes and fostering innovation, suggesting a good operational fit for senior roles requiring both technical and managerial acumen. However, the target role of 'ML Engineer' typically requires hands-on technical depth in ML, which is not explicitly detailed in the experience descriptions, making it difficult to assess the operational fit for a purely individual contributor ML engineering role.