Headline

ML Engineer | Applied AI & MLOps | FastAPI · AWS · XGBoost · RAG

About

I build deployment-oriented machine learning systems focused on APIs, inference workflows, analytics platforms, and cloud-native AI applications. Currently leading AutoCare — an explainable clinical AI platform for Heart Disease, Stroke, and Diabetes risk prediction built on 504K+ healthcare records using XGBoost, SHAP, FastAPI, Docker, and AWS. Previously worked as an AI/ML Intern at GeoTechnoSoft, where I contributed to RAG applications, voice AI systems, ETL analytics platforms, and real-time AI workflows using FastAPI, LangChain, Whisper, WebSockets, and cloud tooling. My primary interests include: • ML Systems Engineering • MLOps & Deployment Workflows • FastAPI & AI Backend Systems • Explainable AI • Cloud-Based ML Infrastructure • Applied AI Engineering Currently preparing for MS in AI/ML Systems (Fall 2027) while continuing to build production-oriented AI systems and strengthen ML infrastructure expertise. Open to: • ML Engineering Internships • Research Collaborations • AI/ML Engineering Discussions • Global Remote Opportunities

Location

Noida

Work experience

ML Engineer & Project Lead — AutoCare at AutoCare (Independent Project), Oct 2025 - May 2026 · 8 mos, Meerut, Leading a team of 4 to build AutoCare — a non-invasive, lifestyle-based Early Risk Screening system for cardiometabolic diseases (Heart Disease, Diabetes, Stroke). - Architected 3 independent ML pipelines on a shared ingestion → validation → transformation → training framework; trained on 192K+ heart, 73K+ diabetes, and 239K+ stroke records. - Heart disease pipeline (Random Forest, F1=0.713, Recall≈1.0) demonstrated strong lifestyle-feature correlation; diabetes and stroke pipelines established baseline performance with identified limitations from class imbalance and absent clinical biomarkers — documented transparently in published proceedings. - Integrated full SHAP explainability layer (TreeExplainer + LinearExplainer) generating per-patient feature contribution scores and top-5 risk factor reports. - Deployed : AWS cloud - Research presented at ICET 26 (BVIMIT Navi Mumbai, April 2026); proceedings indexed on Zenodo — DOI: 10.5281/zenodo.19864529. Two papers produced: one comprehensive review, one original empirical study. Stack: Python · XGBoost · SHAP · Random Forest · Flask · Streamlit · AWS · Docker · Pandas · scikit-learn

AI/ML Intern at GeoTechnoSoft, Aug 2025 - Oct 2025 · 3 mos, Noida, • Delivered 5 production-ready ML/NLP prototypes in 3 months — RAG chatbots, real-time voice assistants, and BI dashboards — all handed over with documentation and modular codebases for team continuity. • Built and deployed an EV Market Analysis platform processing 18+ CSV/Excel datasets through a full ETL pipeline; answered 25+ business intelligence questions via interactive Streamlit dashboards used by non-technical stakeholders. • Architected a bidirectional voice assistant integrating Whisper (STT), FastAPI, and Twilio SDK — processed live caller audio with sub-second transcription latency and real-time TTS response streaming. • Engineered a LangChain RAG chatbot with document ingestion, vectorization, and context-aware query answering; implemented session state management and failover strategies for production-grade uptime. • Maintained production standards across all 5 deliverables: modular architecture, .env-based environment separation, Git branching, and full handover documentation — ensuring team continuity post-internship.

AI-ML Virtual Intern at EduSkills Foundation®, Oct 2024 - Dec 2024 · 3 mos, India, Completed a structured AI-ML virtual internship program under Google for Developers, AICTE, and EduSkills Foundation — one of 10,000+ selected participants nationally. - Completed hands-on labs covering Generative AI, Google Cloud & Vertex AI, Vector Search, Multimodal RAG, and introductory MLOps for GenAI. - Gained practical exposure to prompt engineering, embedding-based retrieval, and responsible AI frameworks on Google Cloud infrastructure. - Applied Vertex AI tools for model deployment and cloud-based AI workflows through guided lab exercises.

Education

Bachelor of Technology, Computer Science, Meerut Institute of Engineering and Technology(MIET)