Data Science with less than a year in Python & Machine Learning
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Scientist and Machine Learning Engineer specializing in end-to-end ML pipelines, deep learning models, and MLOps systems. Experienced in Python, TensorFlow, and scikit-learn with proven expertise in deploying scalable REST APIs and building predictive models that drive business value.
Adani Institute of Infrastructure Engineering, GTU
Bachelor of Engineering · Information and Communication Technology
August 1, 2021 – June 30, 2025
BAPS Swaminarayan Vidyamandir
Higher Secondary Certificate
June 1, 2019 – May 31, 2021
IBM SkillsBuild, CSRBOX Foundation
Data Analytics Intern
June 1, 2024 – August 1, 2024
India
AQI Prediction MLOps Pipeline
January 1, 2023 – January 1, 2023
Built production-grade REST API for real-time Air Quality Index predictions across 29 Indian cities using XGBoost with 8 meteorological features, deployed on Hugging Face Spaces with automated CI/CD pipeline. Optimized model architecture reducing memory footprint by 70% through feature selection and quantization, enabling deployment on free-tier cloud infrastructure without performance degradation. Implemented parallel processing using ThreadPoolExecutor, reducing bulk API response time from 30s to under 3s for batch requests handling 29 cities simultaneously. Established automated model validation processes within the MLOps pipeline, leveraging MLflow and DVC for experiment tracking and version control, ensuring consistent model performance with zero deployment failures over 4 months.
View ProjectAI Interview System
January 1, 2023 – January 1, 2023
Built an end-to-end AI mock-interview platform with resume parsing (PDF/DOCX/TXT), LLM-powered role-aware question generation, and voice-enabled interview flows using TTS (ElevenLabs, gTTS) and ASR (Whisper, Deepgram) provider integrations. Architected a 10-module FastAPI backend with Cassandra-backed session/history persistence, ChromaDB vector store for semantic resume retrieval, and multi-provider LLM routing (Claude, Mistral, Groq, Gemini, OpenRouter). Implemented production-grade MLOps pipeline for NER-based resume entity extraction using DVC + MLflow experiment tracking, with optional Rust-accelerated preprocessing hot paths for parser performance. Deployed full microservices stack on Kubernetes (EKS) with CI/CD via GitHub Actions, Docker, Argo CD GitOps, Prometheus/Grafana observability, Jaeger distributed tracing, and Apache Kafka event streaming.
View ProjectIBM SkillsBuild Data Analytics Certificate
IBM SkillsBuild
January 1, 2024 – Present
The candidate achieved a perfect score (100%) on the 'Data Scientist — Artificial Intelligence' test, indicating exceptional proficiency and mastery of the evaluated skills.
Strengths
Cultural Fit Analysis
The candidate's personal projects demonstrate a high degree of initiative, self-motivation, and a passion for building complex, end-to-end systems. The diversity of projects, from AQI prediction to an AI interview system, showcases a broad interest in applying data science and ML to different domains. The use of modern MLOps practices and cloud-native technologies aligns well with a forward-thinking, agile engineering culture. The internship experience at IBM SkillsBuild also indicates an openness to structured learning and professional development. The candidate's current enrollment in a Bachelor's degree program suggests a continuous learning mindset.
Soft Skills & Operational Fit
The candidate's project descriptions highlight strong problem-solving skills, particularly in optimizing model architecture and deployment. The ability to establish automated validation processes and ensure consistent performance suggests a detail-oriented and reliable approach. Experience in presenting data-driven recommendations indicates good communication skills for technical insights. The psychometric test score of 368/500 suggests a generally positive work attitude and logical reasoning, though specific details on stress handling and team collaboration are not provided.
Limitations