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Assessing your cultural and operational fit
Machine Learning, IOT, RFID, Telecom
https://github.com/sarab96 . Deep Reinforcement Learning, Unsupervised Clustering with Neural Networks Machine Learning with deep CNN networks for face recognition, bridge traffic analysis, number of people in an area from a cctv surviellance cam, etc. Holds five patents in fault tolerant parallel computers and high speed serializer-deserializer circuits Leading NoSQL, Hadoop, OpenStack, SDN, NFV, RFID Assets Management, Time series streaming data analytics teams Handled vendor management (Cadence, Synopsys, Mentor, TSMC, SMIC, UMC etc) and working with strategic partners (Samsung, Tessolve, Bayside) Managed projects with revenue of $4M and IP delivery on $50M projects Executed sale and implementation of RFID Assets management solution for an international hotel chain in India. We also handle software services, outsourcing and staff augmentation with offices in California, NJ and India
Machine Learning by Andrew Ng
Machine Learning by Andrew Ng, Machine Learning
January 1, 2016 – January 1, 2017
Indian Institute of Technology, Kanpur
Bachelor of Technology (BTech), Electrical and Electronics Engineering
January 1, 1992 – January 1, 1996
Reinforcement Learning Project
Deep Reinforcement Learning in Tic-Tac-Toe Game: Machine Learning
February 1, 2017 – March 1, 2017
San Francisco Bay Area
Machine Learning: Clustering Project
Unsupervised Machine Learning: Patient Clustering with Neural Network
January 1, 2017 – March 1, 2017
San Francisco Bay Area
Machine Learning: Face Recognition Project
Deep Convolutional Neural Network for Face Recognition
December 1, 2016 – January 1, 2017
San Francisco Bay Area
Machine Learning: Pedestrians in CCTV Video
Deep Convolutional Neural Network for CCTV Pedestrians
December 1, 2016 – January 1, 2017
San Francisco Bay Area
Machine Learning: RNN LSTM for Predicting Household Electric Consumption
Recurrent Neural Networks for Predicting Household Electric Consumption
December 1, 2016 – January 1, 2017
San Francisco Bay Area
Home Automation Project
Home Automation/Security Project (1 minute video attached)
January 1, 2016 – April 1, 2016
San Francisco Bay Area
IoT Project
IoT Project: Duinomite MicroChip PIC System Development
November 1, 2015 – January 1, 2016
San Francisco Bay Area
Car Health OBD Mobile App Development
Car Health Project: OBD Mobile Application Project
October 1, 2015 – December 1, 2015
San Francisco Bay Area
Hearing Aid: Hearing Helper Mobile App
Hearing Aid Mobile App Project
April 1, 2014 – July 1, 2014
Bengaluru Area, India
Shiksha Infotech Pvt. Ltd.
Machine Learning Enthusiast
April 1, 2014 – Present
San Francisco Bay Area
IBM
Manager Design
May 1, 2011 – February 1, 2014
Bengaluru Area, India
Mouve Electronics
Founder
May 1, 2010 – May 1, 2011
Bengaluru Area, India
Open-Silicon, Inc.
Sr Program Manager
April 1, 2008 – May 1, 2010
Bengaluru Area, India
IBM
R&D Engineer, Manager
August 1, 1996 – March 1, 2008
Albany, New York Area
Machine Learning by Stanford University on Coursera. Certificate earned on Jan 2017
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a very diverse project portfolio, spanning machine learning, IoT, mobile app development, and even prior roles in program management and R&D. This breadth suggests adaptability and a willingness to explore different technical domains. However, the recent experience (since 2014) is heavily focused on self-initiated projects and 'Machine Learning Enthusiast' roles, which might indicate a preference for independent work rather than a traditional corporate team environment. The target role of 'Software Engineer' is broad, and while the candidate has strong ML skills, the lack of recent traditional software engineering roles (e.g., backend, frontend, specific programming languages beyond ML context) might require a specific team fit. The long career history with significant management roles at IBM and Open-Silicon suggests a mature professional, but the recent shift to project-based work needs to be explored for cultural alignment with a structured engineering team.
Soft Skills & Operational Fit
The candidate's project descriptions highlight an ability to work on diverse, self-directed projects, suggesting initiative and problem-solving skills. The detailed descriptions and links to LinkedIn articles/GitHub indicate a proactive approach to documenting and sharing work. However, without specific psychometric or English test scores, it's difficult to assess communication clarity, logical reasoning, work attitude, stress handling, or team collaboration beyond what can be inferred from project descriptions.