Two Years Shipping AI in Production

Design and ship the AI products a performance marketing agency runs for its clients, owning each one from problem scoping and model selection to deployment and iteration.

• Built an AI meeting summarizer that turns hour-long recordings into structured briefs with decisions and action items, cutting follow-up work after every call.
• Shipped lead-qualifying AI chatbots across social and messaging platforms that reply instantly and keep client pipelines active around the clock.
• Developed an NLP keyword analysis engine that surfaces trends, search intent and competitor gaps, replacing gut-feel content planning.
• Delivered a lead management dashboard with real-time pipeline status and full bot conversation history for each client.
• Built a WhatsApp task delegation system where teams assign, track and close tasks by text or voice note, without switching apps.

• Python
• LangChain
• OpenAI API
• WhatsApp Business API
• FastAPI
• React
• PostgreSQL
• Docker

Built ML and deep learning backends that turned large, noisy system logs into alerts that monitoring and reliability teams could act on.

• Built and deployed anomaly detection models, using both ML and deep learning, that flag system irregularities in real time before they escalate.
• Ran EDA on large-scale log datasets and surfaced failure patterns that manual monitoring consistently missed.
• Benchmarked several detection techniques to find the most reliable signals in complex log behaviour.
• Integrated the findings into data processing pipelines and improved the anomaly reporting framework with cross-functional teams.

• Python
• PyTorch
• Scikit-learn
• Pandas
• NumPy
• Deep Learning
• Anomaly Detection
• EDA

Worked in a production data science team, taking projects from raw data to deployed models on Azure.

• Cleaned and preprocessed large datasets so the models trained on reliable inputs.
• Implemented ML models against real business problems, from first experiment to a working predictive model.
• Used Azure cloud services to see how production data applications are architected and deployed.
• Delivered cross-functional project work alongside senior data scientists.

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• Azure
• Machine Learning