Available · New York, USA

Hi, I'm
Monika Patel

Data Scientist

I build intelligent AI systems that turn complex data into real-world impact — from production ML pipelines to LLM-powered applications.

3.5+
Years Experience
92%
Chatbot Accuracy
40%
Fraud Reduction
30%
Throughput ↑
Monika Patel
🤖 LLM & RAG Expert
⚡ Real-Time ML
☁️ AWS · Azure · GCP
01 — About

Turning data into intelligent decisions

I'm a Data Scientist and AI Engineer with 3.5+ years of experience building scalable, production-ready AI systems across the full ML lifecycle — from data pipelines and feature engineering to model deployment and monitoring.

Currently at PTC Inc., I'm engineering RAG pipelines and AI copilots using LangChain and LlamaIndex, while developing time-series forecasting models for IIoT predictive maintenance.

I'm passionate about making AI explainable, reliable, and impactful — bridging cutting-edge research with production reality.

3.5+
Years Experience
92%
Chatbot Accuracy
40%
Fraud Reduction
30%
Throughput ↑
🧬
ML & Deep Learning
TensorFlow, XGBoost, LSTM, CNN, SHAP explainability for production systems.
Generative AI & LLMs
LangChain, LlamaIndex, RAG, LoRA/QLoRA fine-tuning, HuggingFace at scale.
Real-Time Systems
Kafka streaming, Spark, Airflow — sub-second inference at production scale.
☁️
Cloud & MLOps
AWS SageMaker, Docker, Kubernetes, MLflow, full drift monitoring & CI/CD.
02 — Skills

Technical expertise

Proficiency levels
Python & ML Libraries95%
LLMs & RAG Pipelines90%
Cloud & MLOps88%
Deep Learning87%
Data Engineering85%
Data Visualization92%
🐍
Programming
PythonSQLRPandasNumPySciPy
🧬
Machine Learning
Scikit-learnTensorFlowXGBoostLSTMCNNSHAP
Generative AI / LLMs
LangChainLlamaIndexRAGHuggingFaceLoRAAutoGen
Data Engineering
KafkaPySparkAirflowPostgreSQLMongoDBFAISS
☁️
Cloud & MLOps
AWS SageMakerAzure MLGCPDockerKubernetesMLflow
📊
Visualization & BI
TableauPower BIPlotlyMatplotlibSeaborn
03 — Projects

Featured work

PROJECT 01
Real-Time ML Pipeline for Demand Forecasting

Production-grade ML pipeline with Kafka ingestion, Spark processing, Scikit-learn models, MLflow tracking, and REST API predictions with automated drift detection.

📈 Real-time REST predictions · Automated drift detection
KafkaPySparkScikit-learnMLflowFastAPI
View on GitHub →
PROJECT 02
LLM-Based Hybrid Recommender System
🤖

Reviewed 50+ papers on LLM-powered recommenders. Implemented hybrid recommendation using GPT, BERT, T5, LLaMA with collaborative filtering and content-based methods.

🎯 25–40% improvement in precision and recall
GPTBERTT5LLaMACollaborative Filtering
View on GitHub →
PROJECT 03
Intelligent Customer Support Chatbot (RAG)
💬

RAG-powered chatbot with Sentence-BERT embeddings and FAISS vector retrieval for accurate responses across 20+ support categories with hallucination reduction.

✅ 92% query accuracy · 20+ categories · Production
RAGSentence-BERTFAISSLangChainFastAPI
View on GitHub →
04 — Experience

Career journey

Data Scientist
PTC Inc. · Remote, USA
Feb 2026 – Present
  • Building predictive maintenance & time-series forecasting with Python, PySpark, XGBoost for IIoT solutions.
  • Engineering production RAG pipelines and AI copilots using LangChain, LlamaIndex, Hugging Face Transformers.
  • Deploying inference on AWS SageMaker and Kubernetes with MLflow and W&B experiment tracking.
  • Orchestrating high-throughput Kafka and Airflow pipelines on PostgreSQL and MongoDB.
LangChainAWS SageMakerKafkaKubernetesMLflow
ML Research Assistant
Tungsten Automation · Remote, USA
Feb 2025 – Nov 2025
  • Developed supervised learning models with XGBoost and SHAP explainability for non-technical stakeholders.
  • Prototyped RAG pipelines using LangChain and Pinecone for semantic document retrieval.
  • Containerized inference with Docker; tracked experiments via MLflow for reproducibility.
  • Designed Tableau dashboards for model drift and A/B test result monitoring.
XGBoostSHAPLangChainPineconeTableau
Data Scientist
Sigma Infosolutions Ltd. · Mumbai, India
Jun 2021 – Nov 2023
  • Built real-time fraud detection with AWS Kinesis & Lambda — 40% fewer false positives, sub-second response for 1M+ daily transactions.
  • Developed predictive models improving technology planning accuracy by 25% via Docker/Kubernetes.
  • Drove 20% rise in analytics-driven decision adoption through interactive dashboards.
  • Contributed to 30% throughput increase via manufacturing efficiency forecasting.
AWS KinesisLambdaDockerTensorFlowPower BI
05 — Research

Publications & research

🔬
📄 IEEE Xplore · Published Research
Complementary Product Recommendation using Siamese Neural Networks

Enhanced recommendation accuracy and personalization by designing a Siamese Neural Network-based recommender system that learns similarity representations between complementary products. Presented at Aavishkar Inter-college Research Convention.

Siamese NetworksNLPEmbeddingsRecommender SystemsDeep Learning
📄 View on IEEE Xplore →
06 — Education

Academic background

🎓
Master of Science
Information Systems
Pace University
Jan 2024 – Dec 2025 · New York, NY
🏛️
Bachelor of Engineering
Electronics & Telecommunication
St. Francis Institute of Technology
Jul 2019 – May 2023 · Mumbai, India
07 — Credentials

Certifications

🏅Python Data Structures
🏅R for Data Science: Analysis & Visualization
🏅Java Object-Oriented Programming
🏅Quantum Data Analytics Job Simulation
🏅PwC Switzerland Power BI Job Simulation
🏅Customer Churn Analysis Simulation
🏅Data Analytics Job Simulation
🏅Power BI Job Simulation
🏅Data Science Job Simulation
08 — Contact

Let's connect

Open to senior Data Scientist and AI Engineer roles. Let's build something intelligent together.

✉️
Emailmonikapatel65402@gmail.com
💼
LinkedInmonika-patel-844b34228
🐙
GitHubMonikapatel65402
📱
Phone+1 (443) 713-7877
📍
LocationNew York, USA

↓ Download Resume