P Shirisha
Professional Summary
AI/ML Engineer with 0 years of professional experience and strong academic and project background in machine learning and computer vision. Experienced building end-to-end ML solutions using Python, TensorFlow, and Scikit-learn across two capstone projects: a fuel-consumption predictive pipeline and a multi-disease chest X-ray classifier. Skilled in data ingestion and cleaning (SQL, Pandas, NumPy), model development (CNN, transfer learning), evaluation (AUC, RMSE), and reproducible engineering using Git/GitHub. Applied OpenCV for image preprocessing and Matplotlib for model visualization to translate technical results into actionable insights for stakeholders. Seeking an entry-level AI/ML engineering role where I can apply my model-building, data-processing, and prototype delivery skills to production-focused ML problems.
Technical Skills
Work Experience
- Generated and qualified candidate and client leads through targeted sourcing and outreach to maintain a live pipeline of potential placements.
- Conducted detailed requirement-gathering sessions with clients to convert hiring needs into precise job specifications and candidate profiles.
- Designed a standardized candidate-screening checklist and interview brief to improve consistency of shortlists and reduce time to shortlist.
- Maintained and updated the candidate pipeline and client interactions in the CRM to enable accurate tracking and follow-up.
- Partnered with recruiting, operations, and hiring managers to coordinate interviews, feedback loops, and offer processes.
- Prepared and analyzed sourcing channel reports to identify high-yield pipelines and refine outreach strategies for client requirements.
Projects
- Queried and aggregated raw telematics and operational records from SQL sources to construct training and validation datasets.
- Processed and cleaned datasets in Python using Pandas and NumPy, applying feature engineering and outlier handling to improve input quality.
- Trained and tuned Scikit-learn regression models to predict fuel consumption and selected the best model using cross-validation.
- Visualized model predictions and residuals with Matplotlib to communicate model performance and guide feature refinements.
- Published code, preprocessing notebooks, and model artifacts to GitHub with clear documentation to ensure reproducibility.
- Preprocessed chest X-ray images using OpenCV and designed augmentation pipelines to increase dataset diversity and training robustness.
- Architected and trained convolutional neural networks in TensorFlow to classify multiple thoracic diseases from X-ray scans.
- Implemented transfer learning with pretrained backbones and fine-tuning to improve performance on limited labeled data.
- Evaluated models using AUC and confusion matrices and derived a severity scoring heuristic to assist clinical triage decisions.
- Tracked experiments and version-controlled model code and weights on GitHub to maintain reproducible training runs.
Education
Certifications
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