Generative AI Enthusiast | AI Research Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Innovating AI, One Algorithm at a Time✨ I am passionate about leveraging AI for social good and continuously seek opportunities to collaborate with like-minded professionals to make a positive impact. Excited to collaborate and innovate in the AI space! 🚀
Udacity
Nano Degree, AI Programming with Python
June 1, 2024 – October 1, 2024
Hindusthan College of Engineering and Technology
B.Tech Artificial intelligence and machine learning , Artificial Intelligence
November 1, 2020 – December 1, 2024
Nissan Motor Corporation
Gen AI Research Engineer
June 1, 2024 – Present
Chennai, Tamil Nadu, India · On-site
Naan Mudhalvan
Team Lead
March 1, 2024 – June 1, 2024
Coimbatore, Tamil Nadu, India · Hybrid
Centre for Artificial Intelligence & Robotics, DRDO
AI / ML Trainee
July 1, 2023 – June 1, 2024
Bengaluru, Karnataka, India · On-site
Technocolabs Softwares
Machine Learning Engineer
July 1, 2022 – September 1, 2022
India · Remote
ATS Tracking LLM Project with Google Gemini pro
December 1, 2023 – January 1, 2024
Applicant Tracking Systems (ATS) in the hiring process. In today's job market, understanding and optimizing your resume for ATS is no longer optional; it's essential. That's why we're excited to share our latest project: building an ATS Large Language Application, designed to revolutionize how job seekers craft their resumes.
DISEASES DETECTION SYSTEM FOR LIVESTOCK
May 1, 2023 – Present
The economic stability of agricultural communities, animal welfare, and global food security are all seriously threatened by livestock illnesses. Systems for detecting effective diseases are essential to reducing these difficulties. Clinical observation has long been the cornerstone of illness identification in livestock, allowing skilled veterinarians to evaluate physical signs and behavioural abnormalities. This method is not without its drawbacks, though, especially when it comes to diagnosing illnesses in their early stages or differentiating between minute differences in symptoms. This environment has completely changed due to recent breakthroughs in diagnostic technology, which provide faster and more accurate ways to identify diseases. The polymerase chain reaction (PCR) and the enzyme-linked immunosorbent assay (ELISA) are two examples of modern molecular techniques that allow for the precise and sensitive detection of particular diseases. Furthermore, the introduction of next-generation sequencing has made it easier to do thorough genomic analyses, which has improved our knowledge of the hereditary causes of many illnesses and aid in the creation of focused interventions. In addition to conventional approaches and molecular techniques, sensor-based monitoring systems have become effective instruments for ongoing animal health surveillance. Wearable technology with built-in sensors may track important metrics like heart rate, body temperature, and activity patterns.
Startup Acquisition Analysis and Prediction
July 1, 2022 – September 1, 2022
The dataset is all about the Startup's Financial Information and we need to predict the current financial status of the company. The database is Crunchbase-2013, database, company, investors and all the other details belong to the company. In this project, we analyzed the company's status i.e, Operating, Acquired, IPO, or closed. The dataset is completely based on the company's information from the company's website in 2013. We deleted with redundancy, granularity, and irrelevant information we also deleted instances with missing values for specific columns we removed NULL values for the following columns status, country_code, and Founded_at. The imputing method uses mean, median, and mode for the columns with less percentage of NULL values. Then we removed outliers using the IQR method then we generalized category_code and country_code using One hot encoder to prepare for feature engineering by working on our targeted variable Status and we finalized the data analysis part by deleting duplicates from datasets. We started the ML part by applying the Logistic Regression Model, XGBoost Classifier Model, Quadratic Analysis Model, and Random Forest Classifier Model. But first, we had to prepare the data through the following steps.
Generative AI
Google Cloud Skills Boost
June 23, 2026 – Present
AI Programming with Python
Udacity
June 23, 2026 – Present
Foundation Course on Generative Ai
Microsoft
June 23, 2026 – Present
AWS Cloud Practitioner
Amazon Web Services (AWS)
June 23, 2026 – Present
Enterprise Design Thinking - Team Essentials for AI
IBM
June 23, 2026 – Present
Enterprise Design Thinking Practitioner
IBM
June 23, 2026 – Present
Supervised Machine Learning: Regression and Classification
Stanford University
June 23, 2026 – Present
Machine Learning Engineer
Technocolabs Softwares Inc.
June 23, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from agricultural applications to financial predictions and LLM development, shows a broad interest in applying AI/ML across different domains. The experience at DRDO and Nissan suggests an interest in impactful and research-oriented roles. The certifications from Google, Microsoft, AWS, IBM, and Stanford indicate a proactive approach to learning and professional development, which aligns with a culture of continuous improvement. However, the experience is relatively short, and the target role of ML Engineer is well-aligned with their academic and project background.
Soft Skills & Operational Fit
The candidate's project descriptions suggest an ability to work on complex problems and a foundational understanding of project phases. The 'Team Lead' role at Naan Mudhalvan indicates some leadership potential. However, without specific psychometric test results or interview data, a detailed assessment of soft skills like communication, stress handling, and team collaboration is not possible. The provided assessment scores for English and Psychometric tests are 0, indicating no data for these areas.