Reenu Philip
AI Engineer
Trivandrum
Summary
Dynamic AI Engineer with expertise in Python and Generative AI, recognized for enhancing operational efficiency through the development of intelligent systems for multimodal data analysis and real-time inventory management. Proven problem-solving skills facilitate the delivery of innovative solutions that drive business success.
Overview
4
4
years of professional experience
Work History
AI Engineer
EY
03.2022 - 10.2025
- Designed and implemented Python-based object detection pipelines using the YOLO model, enabling automated identification of logos, products, and visual elements within Dell's marketing and product content. This significantly reduced manual review effort and increased accuracy.
- Built end-to-end image and text detection workflows using LLaVA and LLaMA multimodal models, enabling rich visual-text understanding. These pipelines extracted textual elements from images, detected brand usage, and validated compliance against Dell's internal marketing guidelines.
- Developed an audio extraction and analysis module using the Mistral-7B model, allowing the system to convert speech to text, derive contextual meaning, and validate whether spoken audio aligned with Dell's messaging standards.
- Integrated all model outputs-image, text, and audio-into a unified GenAI platform, which served as Dell's internal AI-driven content evaluation tool. This platform brought multimodal insights together, enabling automated decision-making and consistent branding review.
- Optimized model calling, batching, and parallel processing in Python, ensuring high throughput and low latency across the entire GenAI pipeline.
- Designed and deployed a multi-agent system using LangChain and LangGraph to automate workflows involving reasoning, decision-making, and tool execution.
- Developed specialized AI agents to analyze inventory datasets, including SKU details, stock levels, expiry dates, and movement history.
- Assigned each agent a dedicated responsibility such as near-expiry detection, overstock/understock analysis, and automated stock-transfer recommendations.
- Implemented autonomous decision logic allowing agents to read data from APIs/Databases, perform LLM-based reasoning, and trigger actions like email alerts, stock-transfer order creation, and inventory record updates.
- Integrated the agent system into a customer-facing dashboard that enabled retail users to query inventory in natural language and receive real-time insights (e.g., 'Show me items that will expire in the next 30 days', 'Transfer 20 units of SKU123 to the Bangalore store.').
- Delivered an end-to-end agentic solution that significantly reduced manual analysis time, improved stock accuracy, and enabled faster, data-backed retail decision-making.
Education
M.Tech - Image Processing
APJ Abdul Kalam Technological University
Kerala03-2018
B.Tech - Computer Science
Cochin University of Science And Technology
Kerala05-2016
Skills
Python
SQL
Pandas
NumPy
MLflow
Matplotlib
Plotly
PyTorch
TensorFlow
YOLO
LLaVA
LLaMA
Mistral 7B
GPT 4
Generative AI
Agentic AI
LangChain
LangGraph
LlamaIndex
Hugging Face
Audio Processing
Azure
FastAPI
Git
Timeline
AI Engineer
EY
03.2022 - 10.2025
B.Tech - Computer Science
Cochin University of Science And Technology
M.Tech - Image Processing
APJ Abdul Kalam Technological University