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AI Engineer with 1+ years in Machine Learning & Generative AI
AI/ML and Generative AI professional with experience in Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Vector Databases for healthcare and life sciences applications. Skilled in developing predictive models and AI-driven solutions using Python, R, SQL, and Generative AI technologies for drug discovery, drug safety prediction, and biomedical data analysis. Experienced in integrating molecular and gene expression datasets (GEO, ChEMBL, DrugBank), performing feature engineering, and applying cheminformatics techniques using RDKit and SMILES-based modeling. Built ML and GenAI models including Random Forest and XGBoost, achieving up to 85% accuracy in drug side-effect prediction and research automation workflows. Hands-on experience in developing PPI, Drug–Gene, and Drug-Drug Interaction networks using STRING and Cytoscape, along with data visualization using Matplotlib, Seaborn, and Excel. Certified in AI/ML in Biology, Bioinformatics & Computational Biology (Biotecnika, 2025) and ISTQB Foundation Level (CTFL), with a strong interest in AI-powered healthcare innovation.
JNTU Kakinada
M.Tech · Biotechnology
August 1, 2013 – June 30, 2015
V.S.M College
M.Sc · Biotechnology
August 1, 2010 – June 30, 2012
Swiftsoft Infotech Services Pvt. Ltd.
AI/ML & Generative AI Engineer
November 1, 2024 – Present
India
Sri Chaitanya Educational Institutions
Head of Department – Biology
March 1, 2022 – September 1, 2024
India
Predictive Modeling of Side Effects in Anti-Diabetic Drugs Using Machine Learning, Generative AI & LLMs
November 1, 2024 – June 1, 2026
Developed an AI-driven predictive model to analyze and predict side effects of anti-diabetic drugs, including Metformin, Pioglitazone, Glipizide, Sitagliptin, and Dapagliflozin. Integrated molecular data from ChEMBL and gene expression data from GEO using Python, R, SQL, and bioinformatics workflows. Performed data preprocessing, feature engineering, and molecular descriptor generation using RDKit and SMILES-based representations. Built and evaluated Machine Learning models including Random Forest, Logistic Regression, and XGBoost, achieving up to 85% prediction accuracy. Leveraged Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG) techniques for knowledge retrieval, biological insight extraction, and research automation. Applied Generative AI methods for synthetic data generation, feature augmentation, and AI-assisted analysis of biomedical datasets. Constructed Protein-Protein Interaction (PPI), Drug-Gene, and Drug-Drug Interaction networks using STRING and Cytoscape for drug safety and repurposing analysis. Utilized vector database concepts and AI-powered retrieval workflows to enhance access to biomedical knowledge and literature. Developed visualizations and analytical reports using Pandas, NumPy, Matplotlib, Seaborn, and Excel. Identified key molecular descriptors and cardiovascular and gastrointestinal risk factors associated with anti-diabetic therapies.
ISTQB Foundation Level (CTFL)
ITB India
June 1, 2026 – Present
AI/ML in Biology, Bioinformatics & Computational Biology
Biotecnika
January 1, 2025 – Present
Coding for Biologists
Biotecnika
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's background in Biotechnology combined with recent AI/ML and Generative AI experience demonstrates a strong drive for interdisciplinary application, which is a positive cultural fit for innovative AI engineering roles. The project's focus on healthcare and life sciences aligns with specialized AI domains. The breadth of skills from bioinformatics to advanced AI techniques suggests adaptability and a continuous learning mindset. However, the recent transition into a dedicated AI role means less direct industry experience in a pure software engineering context, which might require some adjustment to a fast-paced tech environment.
Soft Skills & Operational Fit
The candidate's project description indicates an ability to work on complex, interdisciplinary problems, suggesting strong problem-solving and analytical skills. The previous role as 'Head of Department' implies leadership, mentoring, and communication skills, which are valuable for team collaboration and project management. The focus on AI-powered healthcare innovation aligns well with a forward-thinking operational environment.