Kiran Shekarappa
Machine Learning EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Strategy and Enablement | End to End Machine Learning & AI Delivery | Solutions Architect | Technology Enthusiast || Technical Lead | Innovator | Student of Yoga, Ayurveda and Vedic Philosophy
Over 10 years of experience in Heavy Duty Off-road Power System development . Specialist in developing Machine Learning & AI Solutions to solve engineering problems. Academic background in Mechanical Engineering. Domain knowledge expertise in Engines, Aftertreatmement, Controls and Calibration. Skilful in applying Data Science methods/technology stack to transform Power System technology development. Lead projects, teams and organisational aspirations.
University of Michigan
Master's in Automotive Systems Engineering, Internal Combustion Engines
January 1, 2013 – January 1, 2015
Visvesvaraya Technological University
Bachelor of Engineering (B.E.), Mechanical Engineering
January 1, 2009 – January 1, 2013
John Deere
Senior Engineer- Machine Learning & AI
May 1, 2025 – Present
On-site
John Deere
Technology Lead- Analytics, Machine Learning & AI
April 1, 2023 – Present
On-site
John Deere India Pvt. Ltd. (JDTCI)
Senior Technical Lead
October 1, 2018 – March 1, 2023
Pune, Maharashtra, India
Cummins Inc.
EBU Aftertreatment Integration- Senior Engineer
July 1, 2017 – July 1, 2018
Columbus, Indiana
John Deere
Aftertreatment Development Engineer- APSE Innovation (Infotree Service Inc)
June 1, 2015 – June 1, 2017
Waterloo, Iowa Area
FEV North America, Inc.
Engineering Assistant
July 1, 2014 – April 1, 2015
Greater Detroit Area
Simulation and Analysis of a medium speed, lightly turbocharged, DI Diesel Engine
January 1, 2014 – April 1, 2014
Simulated a 5.18 L, 6 cylinder, lightly turbocharged diesel engine to estimate their Pressure, Temperature relationships with respect to crank angle for two different operating conditions. The simulation was done from, the start of compression stoke to the end of expansion stroke, considering various factors like, Ignition delay, heat transfers through the walls, change in specific heats with temperature, premixed and diffusion burning phases, variation in temperatures and pressures due to the exhaust valve opening 30 degree BBDC. Finally, energy balance was done on the system, to estimate the energy distribution through the entire system.
Sheet Metal Hydro-Forming of closure panels
December 1, 2013 – Present
Formulated Sheet Hydroforming process to manufacture vehicle door panels using dual phase steels as the material. Tool design and the parameters influencing the quality of the products were discussed.
Process Plan to Implement Minimum Quantity Lubrication (MQL)
November 1, 2013 – Present
Implemented Minimum Quantity Lubrication for machining an automotive engine block, while introducing the latest scientific knowledge on the process. The frictional, thermal and geometrical effects were studied thoroughly. Part material consideration and other issues related to tool wear were addressed.
Waste Heat Recovery from Engine Coolant
November 1, 2013 – Present
Evaluated different methods to recover heat from the engine coolant and convert it to useful work. The aim of the project was to increase the fuel efficiency of a full scale, 4-Stroke- 5.0 liter, passenger car by utilizing the lost heat at the engine coolant. Different systems were considered and Thermoelectric Generators was selected among them. A detailed design of the system was developed and the process was simulated to test for increase in fuel economy by adopting this system.
Process design to Cast an Exhaust Manifold
October 1, 2013 – Present
Designed a process for Sand Casting an automobile exhaust manifold using Silicon-Molybdenum alloy as the material. The potential defects of the cast products were discussed and the challenges involved in this process were addressed carefully. Other issues such as environmental impact of the process and the validity of the process for mass production were also enumerated.
Design and Development of a Low Cost-Stirling Engine
June 1, 2012 – April 1, 2013
Designed and Fabricated a Low cost- Stirling Engine, with a power output of 200W. A prototype Stirling Engine was fabricated and the drive mechanism was tested successfully. The study aimed to utilize the dry organic wastes abundantly available in the rural areas of India as the source of heat to the engine.
Automotive Design of a Radio Controlled Car
October 1, 2010 – Present
Designed and Fabricated a Radio Controlled off road car (1/8 scale) for an inter-collegiate racing competition held at National Institute of Technology, Surathkal, Karnataka.
Cultural Fit Analysis
The candidate's project history is heavily focused on mechanical and automotive engineering, with a strong emphasis on internal combustion engines and related systems. While there is a clear career pivot towards Machine Learning and AI within John Deere, the personal projects do not reflect this transition. The target role is 'Machine Learning Engineer', but the project diversity does not directly align with typical ML engineering portfolios (e.g., no projects involving ML model development, deployment, or specific ML frameworks). This suggests a potential gap in demonstrating hands-on ML project experience outside of their recent professional roles. The breadth of skills listed in the resume is also limited, with 'Technical Leadership' being the only explicit skill mentioned in recent ML/AI roles, which makes it difficult to assess the depth of their ML technical skills.
Soft Skills & Operational Fit
The candidate's career progression from core engineering to data analytics and ML/AI roles suggests strong adaptability, a proactive learning attitude, and leadership potential. The descriptions of leading data analytics competency and providing technical mentorship indicate good communication and collaboration skills. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.