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Assessing your cultural and operational fit
AI Engineer with 1+ years in ML, GenAI, and RAG pipelines.
AI Engineer with 2 years of hands-on experience developing and deploying machine learning models and GenAI solutions in production environments. Proven expertise building end-to-end RAG (Retrieval-Augmented Generation) pipelines, fine-tuning LLMs, and implementing vector databases for semantic search and retrieval applications. Strong foundation in supervised/unsupervised ML (regression, classification, clustering, forecasting), data preprocessing, and feature engineering using Python (pandas, NumPy, scikit-learn, TensorFlow, Keras). Experienced deploying AI models on cloud platforms (GCP Vertex AI) with Docker containerization. Skilled in collaborating with cross-functional stakeholders to translate business requirements into AI-driven solutions and presenting technical results to both technical and non-technical audiences.
Loyola Academy
Master of Science · Data Science
October 1, 2022 – June 1, 2024
Loyola Academy
Bachelor of Science · Mathematics, Statistics & CS
June 1, 2019 – July 1, 2022
StableLab
AI Engineer / Data Engineer
August 1, 2024 – March 1, 2026
Hyderābād, Telangana, India
Innomatics Research Lab
ML Engineer Intern
December 1, 2023 – February 1, 2024
Hyderābād, Telangana, India
Food Delivery Time Prediction
June 27, 2026 – Present
Developed end-to-end ML pipeline for delivery time prediction using 15 operational features (distance, weather, traffic, driver metrics), applying feature engineering, model selection, and ensemble methods to optimize prediction accuracy.
Text Classification & NLP
June 27, 2026 – Present
Implemented text classification models using TF-IDF, word embeddings, and basic transformer architectures for sentiment analysis and document categorization tasks.
Cultural Fit Analysis
The candidate's experience spans academic projects, an internship, and a full-time role, showcasing a diverse learning and application background. Their work on GenAI, ML, and data engineering projects aligns well with the target role of an AI Engineer. The breadth of skills listed, from core ML fundamentals to cloud platforms and MLOps, indicates adaptability and a willingness to engage with various aspects of AI development. The focus on optimizing LLM token consumption and improving retrieval efficiency suggests a practical, resource-conscious mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through their experience in deploying production-grade systems, implementing robust error handling, and maintaining pipeline uptime. Their collaboration with researchers and analysts, and communication of technical findings to non-technical stakeholders, indicate good soft skills for a team environment. The detailed project descriptions suggest a methodical approach to problem-solving and a focus on practical, deployable solutions.