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that's me! 👋
✂️ pasted fresh into the scrapbook —
Ilesh Patel
Artificial Intelligence Engineer
what i build & deliver
Engineering Services & Solutions
End-to-end AI capabilities — from mathematical formulation and model training to high-availability web deployment.
AI Systems
Generative AI & RAG Pipeline Engineering
Designing production-grade Retrieval-Augmented Generation (RAG) systems with hybrid semantic search, cross-encoder rerankers, structured Pydantic outputs, and hallucination guardrails.
Key Deliverables:
- ✦Context-Aware Document Q&A Engines
- ✦Vector Similarity & Dense Embedding Search
- ✦Hallucination Mitigation & Output Validation
- ✦LLM Agent Tool-Calling Workflows
PythonFastAPISentence-TransformersFAISSLangChainOpenAI / Claude APIs
Model Development
Production Machine Learning & Deep Learning
End-to-end predictive modeling from exploratory data analysis and feature engineering to model training, cross-validation, and error attribution.
Key Deliverables:
- ✦Supervised Classification & Regression
- ✦Time-Series Forecasting & LSTM Networks
- ✦SHAP Explainability & Risk Attribution
- ✦Imbalanced Class Handling Strategies
PyTorchTensorFlowScikit-LearnXGBoostPandasNumPy
Full Stack
Full-Stack Intelligent Web Applications
Building high-performance, modern, and accessible web applications that integrate AI inference backends with intuitive frontend user experiences.
Key Deliverables:
- ✦Next.js & React Web Applications
- ✦RESTful API Prediction Microservices
- ✦Interactive Data Dashboards & Visualizations
- ✦Secure Authentication & State Management
Next.jsReactTypeScriptTailwind CSSNode.jsExpressStreamlit
IoT & Edge AI
IoT Anomaly Detection & Edge ML
Deploying lightweight ML models to edge devices and streaming telemetry pipelines with real-time sensor anomaly detection using Variational Autoencoders.
Key Deliverables:
- ✦Variational Autoencoder (VAE) Anomaly Models
- ✦Edge Model Quantization (TFLite / ONNX)
- ✦Real-Time Telemetry Sensor Pipelines
- ✦Industrial Hardware Monitoring Architecture
PyTorchONNXTFLiteFastAPIMQTTPython
Fintech & Analytics
Quantitative Risk & Financial Modeling
Developing quantitative risk prediction engines, financial sentiment analysis pipelines, and algorithmic market forecasting models.
Key Deliverables:
- ✦Liquidity Shortage Risk Scoring Engines
- ✦Market Trend & Moving Average Predictions
- ✦Financial News NLP Sentiment Scoring
- ✦Interactive Financial Dashboards
PythonXGBoostKerasPlotlyyfinanceVADER
MLOps & Deployment
Model Serving & Low-Latency API Services
Packaging trained AI/ML models into containerized, low-latency microservices with automated input validation, rate limiting, and operational monitoring.
Key Deliverables:
- ✦FastAPI & Flask REST Microservices
- ✦Dockerized Container Deployment
- ✦Async Inference Batching & Caching
- ✦Sub-100ms API Latency Optimization
FastAPIDockerUvicornPydanticPythonLinux
things I live by
The Rule Book
rules for building
- 1.
evaluate and validate model performance before jumping into deployment.
- 2.
clean data and solid feature engineering beat overly complex architectures every time.
- 3.
design end-to-end AI systems with explainability and human-in-the-loop oversight.
& rules for me
- 4.
RAG without proper chunking and vector search relevance is just noise.
- 5.
keep inference latency low and APIs RESTful and responsive.
- 6.
stay curious — the field of AI and Machine Learning reinvents itself continuously.
— IleshDevX ✌️
scribbled over the years — still holding up 📖
from the notebook