Summary
I'm an ML engineer and applied AI developer passionate about building robust and ethical AI systems. My work spans NLP, computer vision and multi‑modal models, and I enjoy solving practical problems by combining deep learning with strong systems engineering. I have experience developing retrieval‑augmented generation workflows with prompt optimisation, deploying transformer‑based models for document classification, entity extraction and information retrieval, and delivering end‑to‑end ML pipelines with Docker.
Experience
AI Engineer Intern – Neolen
Remote · Nov 2025 – Feb 2026
- Built a multi-agent system using LangGraph and prompt optimisation for a diabetes-reversal health-tracking platform.
- Optimised agent routing logic, reducing average response latency by 47% and eliminating loop failures in the workflow.
Software Engineering Intern – OHack
Remote · Jun 2025 – Aug 2025
- Deployed an NLP-based document-classification agent using transformer models with FastAPI and Google API.
- Engineered a text-classification pipeline with structured logging for model iteration, traceability and production monitoring.
Junior ML Engineer & Data Engineering Lead – Omdena, ClimateSense
Remote · Jun 2025 – Sept 2025
- Led a data engineering pipeline that unified ERA5, GPM, DEM and HydroSHEDS datasets into analysis-ready stores; implemented validation checks and anomaly-detection screens.
- Built and deployed a deep-learning weather-forecasting model (GraphCast) for flood-risk prediction and containerised reproducible inference with Docker.
Junior ML Engineer & AI Lead – Omdena, CarbonAgents
Remote · Jul 2025 – Oct 2025
- Designed a LangChain/LangGraph multi-agent system with RAG for small and medium-enterprise carbon accounting: OCR invoices → NER/entity extraction → Scope 2 emissions → report generation.
- Implemented prompt engineering strategies and retrieval optimisation for accurate LLM-based document processing; containerised services with Docker.
Technologies
Languages: Python, C, C++, SQL.
ML/DL frameworks: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, Pandas, NumPy, Matplotlib.
GenAI frameworks: Transformers, spaCy, LangChain, LangGraph, LangSmith.
Computer vision tools: YOLO, OpenCV, object detection, image classification, multi-modal processing.
Databases & Search: FAISS, Elasticsearch, semantic embeddings, vector similarity search.
Deployment & DevOps: Docker, FastAPI, Git, REST APIs.
Education
Heritage Institute of Technology
B.Tech in Electronics & Communication Engineering · Sept 2022 – May 2026
Grade: 8.95 / 10.
Relevant coursework: Machine Learning, Deep Learning, Probability & Statistics, Linear Algebra, Data Structures & Algorithms.