Help & Supportcareers@gravityer.com
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with 1+ years in Machine Learning and Deep Learning solutions
AI/ML Engineer with around 2 years of experience in designing and deploying end-to-end Machine Learning and Deep Learning solutions across real-world domains. Proficient in building predictive models using Python, Scikit-learn, and deep learning frameworks, with strong expertise in regression, classification, and clustering techniques including Random Forest and XGBoost. Experienced in developing ANN, CNN, RNN, and LSTM models for Computer Vision and NLP applications. Skilled in feature engineering, hyperparameter tuning, and model optimization using cross-validation. Familiar with Generative AI technologies including Transformers, LLMs, Hugging Face, and LangChain, along with working knowledge of MLOps and model lifecycle management.
Shivaji University
BCS
N/A – June 30, 2025
New College, Kolhapur
HSC
N/A – May 31, 2022
New High School, Bachani
SSC
N/A – May 31, 2020
Belatrix Consultancy Services Pvt. Ltd.
AI/ML Engineer
April 1, 2025 – Present
Pune, Maharashtra, India
AI/ML-Based Pharmaceutical Analytics & Process Optimization System
April 1, 2025 – June 1, 2026
Developed an AI/ML-based analytics system to optimize pharmaceutical manufacturing processes by analyzing batch records, laboratory reports, and quality control data. Built predictive models to identify potential quality deviations, detect batch failures, and improve process efficiency. Deployed scalable solutions to support data-driven decision-making in production and quality management. Analyzed pharmaceutical manufacturing data from batch records (BMR), lab reports, and quality control datasets to build structured datasets. Performed data cleaning, preprocessing, and transformation using Pandas and NumPy, handling missing values, outliers, and inconsistencies to improve data quality. Developed and optimized predictive models (Random Forest, XGBoost, SVM) for detecting batch failures and quality deviations. Applied clustering techniques to identify patterns and insights in production processes and parameters. Performed feature engineering and improved model accuracy using hyperparameter tuning and cross-validation. Evaluated model performance using metrics such as Accuracy, Precision, Recall, F1-Score, RMSE, and R2. Built and deployed interactive ML applications using Streamlit for real-time predictions and data visualization. Deployed scalable ML solutions using Docker and AWS cloud infrastructure. Created visualizations and analytical reports using Matplotlib and Seaborn to support decision-making. Delivered data-driven insights to improve product quality and manufacturing efficiency.
AI/ML-Based Predictive Analytics & Operational Optimization System
July 1, 2024 – March 1, 2025
Developed an AI/ML-based predictive analytics system to optimize manufacturing operations by analyzing production, quality, and sensor data. Built machine learning and deep learning models to predict product quality, detect defects, and forecast production performance. Deployed scalable solutions using Streamlit, Docker, and AWS, enabling data-driven decision-making and improved operational efficiency. Built end-to-end ML pipeline using production logs, quality data, and sensor datasets for predictive analytics. Performed data preprocessing, feature engineering, and transformation using Pandas and NumPy. Developed classification and regression models (Random Forest, XGBoost, SVM) to predict product quality and manufacturing defects. Implemented K-Means clustering to identify operational patterns and anomalies in production data. Designed deep learning models (ANN, LSTM) for time-series forecasting of production yield and equipment performance. Improved model performance using hyperparameter tuning (GridSearchCV) and cross-validation. Evaluated models using metrics such as Accuracy, F1-Score, RMSE, and R2. Built and deployed interactive ML applications using Streamlit for real-time predictions and data visualization. Deployed scalable ML solutions using Docker and AWS cloud infrastructure. Delivered data-driven insights and visualizations to support operational decision-making.
Cultural Fit Analysis
The candidate's projects demonstrate a focus on practical, industry-specific AI/ML applications, which aligns well with a results-oriented culture. The use of Agile methodologies suggests adaptability and teamwork. The breadth of technical skills, including both traditional ML and emerging Generative AI, indicates a proactive learning mindset. However, the limited professional experience (starting April 2025) and ongoing education suggest a need for mentorship and growth within a team.
Soft Skills & Operational Fit
The candidate's resume highlights collaboration with cross-functional teams in an Agile environment, indicating good team collaboration and operational fit. The project descriptions are detailed, suggesting a structured approach to problem-solving and project execution. However, without direct assessment data on communication or psychometric traits, a deeper analysis of soft skills is limited.