AI Engineer with less than a year in Data Science & Machine Learning
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Assessing your cultural and operational fit
AI/ML Engineer skilled in developing data-driven solutions using Python, Machine Learning, and Deep Learning frameworks. Experienced in building end-to-end ML pipelines, optimizing model performance. Proficient in TensorFlow, LangChain, RAG-based systems, and cloud deployment. Passionate about transforming complex data into scalable, production-ready AI solutions.
Savitribai Phule Pune University | Pravara Rural Engineering College, Loni
Bachelor of Engineering · Information Technology
August 1, 2020 – June 30, 2024
Rubixe
Data Scientist Intern
March 1, 2025 – November 1, 2025
India
Loan Product Assistant
June 25, 2026 – Present
Built an Loan Product Assistant using LangChain and Retrieval Augmented Generation (RAG) to answer queries from bank loan documents. Processed Bank of Maharashtra PDFs by chunking text with Recursive Character Text Splitter and generating embeddings using OpenAI Embedding models. and retrieved document embeddings efficiently using a FAISS vector database for semantic search. Ensured accurate, non-hallucinated responses by grounding LLM outputs strictly in retrieved document content
Spare Parts Inventory Forecasting - Client: NewX Service
June 25, 2026 – Present
Developed a spare parts demand forecasting system using historical service data to optimize inventory management. Pre-processed MySQL data (missing values, outlier treatment), performed time-series analysis, and trained ARIMA, SARIMAX, and Exponential Smoothing models. Achieved ~17% MAPE with SARIMAX and deployed the model using AWS S3 and Fast API for API-based predictions.
Cultural Fit Analysis
The candidate's projects demonstrate a proactive approach to learning and applying diverse AI/ML technologies, from RAG-based systems to time-series forecasting. The 'Loan Product Assistant' project shows an interest in practical applications of LLMs, while the 'Spare Parts Inventory Forecasting' project indicates a focus on business optimization. This diversity suggests adaptability and a willingness to tackle different types of problems, which aligns well with a dynamic AI engineering role. The remote internship also points to self-discipline and effective communication in a distributed setting.
Soft Skills & Operational Fit
The candidate's internship experience at Rubixe highlights collaboration with cross-functional teams and stakeholders, indicating an ability to work in a team environment and align technical work with business objectives. The project descriptions suggest a problem-solving approach and attention to detail in data handling and model deployment. However, without direct assessment data, the depth of these soft skills and operational fit cannot be fully determined.