AI Engineer

Experience

AI Engineer — Nov 2025 – Present

• Engineered and scaled a RAG-based customer support chatbot to 200+ daily users, utilizing n8n for orchestration, Qdrant for vector storage, and Langfuse for observability and performance tracking.

• Mentored junior AI Engineers on best practices for LLM deployment and observability, increasing team velocity and ensuring high standards for code maintainability.

• Collaborated with cross-functional stakeholders to define technical specifications for AI initiatives, ensuring an alignment between business objectives and engineering execution.

Machine Learning Researcher — Apr 2025 – Sep 2025

• Processed custom, noisy real-world datasets using SQL for querying and transformation, combined with data cleaning, labeling, and feature extraction, preparing datasets for machine learning pipelines, including training, evaluation, and deployment.

• Trained classical machine learning models (e.g., Random Forest, XGBoost, CatBoost) as well as deep learning models with PyTorch, achieving 30% higher accuracy compared to the baseline models.

• Integrated Weights, & Biases (W&B) for experiment tracking, performance visualization. As well as Docker and FastAPI for model inference.

Machine Learning Engineer — Oct 2023 – May 2024

• Designed and deployed a containerized inference pipeline for LLMs using FastAPI, Docker, and HuggingFace Transformers, enabling real-time performance on visual reasoning tasks. Track progress with Weights, & Biases and trained models on multiple GPUs.

• Worked extensively with LLMs and vision-language models (VLMs) to build AI systems capable of interpreting and reasoning about complex multimodal data.

• Built a complete ML pipeline from data loading and preprocessing to training and LLM inference on a server, leveraging NumPy, Pandas, and advanced SQL for data wrangling, used Matplotlib and Seaborn for visualization, and PyTorch for model development.

Software Developer — Jun 2021 – Dec 2022

• Grew the number of active users to 5,000 by implementing new features and improving app performance based on user feedback and analytics

• Collaborated with cross-functional teams developers, designers, and stakeholders to ensure alignment with business objectives and successful project delivery.

• Led end-to-end projects from ideation stage to the production, identifying essential features and aligning development with user needs and business strategy.