
VP Engineering, Agentforce at Salesforce
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Expertise in AI, NLP, and Machine Learning product development. I care deeply about shipping innovative products that customers love. Products that solve for a customer job to be done * Applied R&D leader with 20 years of AI/ML experience * Instrumental in hiring and building Machine Learning, Engineering and Data Science teams for over 10 years * Provides mentorship and leads managers and ICs at all levels with effective feedback and coaching * Results driven leadership where impact is scientifically measured through KPI improvements
Johns Hopkins Whiting School of Engineering
M.S.E, Computer Science
January 1, 2006 – January 1, 2007
International Institute of Information Technology Hyderabad (IIITH)
B. Tech (Hons.), Computer Science Engineering
January 1, 2002 – January 1, 2006
Salesforce
VP of Software Engineering
August 1, 2025 – Present
Salesforce
Sr Director
August 1, 2021 – July 1, 2025
Salesforce
Director, ML Applications
July 1, 2019 – July 1, 2021
MZ
Director, AI/ML
March 1, 2014 – July 1, 2019
MZ
Machine Learning & NLP Lead
June 1, 2012 – February 1, 2014
Pearson
Sr. Research Scientist
January 1, 2008 – June 1, 2012
San Francisco Bay Area
Johns Hopkins University
Research Assistant
September 1, 2006 – August 1, 2007
Baltimore, Maryland Area
Gamification
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong background in AI/ML and software engineering leadership, which aligns with a high-impact technical culture. However, the target role is 'Data Analyst', which is a significant shift from their senior leadership and engineering focus. While they possess strong analytical skills from their past roles (Data Mining, Data Analysis, ML applications), the direct alignment with a pure 'Data Analyst' role, which typically involves less leadership and more hands-on data manipulation and reporting, is not a perfect fit. Their experience is more geared towards leading and building AI/ML systems rather than traditional data analysis.
Soft Skills & Operational Fit
The candidate's extensive experience in leadership roles, driving product vision, and working with cross-functional partners indicates strong communication, collaboration, and strategic thinking skills. Their background in building and leading teams suggests strong operational fit for managing technical initiatives.