$ python train_model.py
↳ Loading pipeline… done
↳ Model accuracy: 94.2%
$ deploy --env prod
↳ Status: deployed ✓
$ |
A results-driven data professional with expertise in building robust ETL pipelines and automated workflows that transform raw data into actionable insights. Proven track record of reducing manual processing time by 60% and improving system reliability by 40% through intelligent automation using Python, SQL, Apache Spark, and cloud platforms. Specializes in designing scalable data integration pipelines, managing both structured (PostgreSQL, SQL) and unstructured data (vector databases, NoSQL), and implementing real-time data synchronization for enterprise systems. Experienced in big data technologies including Hadoop and Spark, with hands-on skills in creating interactive visualizations and performing exploratory data analysis to uncover critical business patterns. Currently pursuing a Master's in Applied Machine Intelligence at Northeastern University while bringing a unique combination of technical expertise in cloud platforms (AWS, Azure), API integrations, and strong business acumen to deliver data solutions that drive strategic decision-making and measurable organizational impact.
Northeastern University | Boston, USA | 2024 - 2026
Currently completing Masters in Professional Studies at Northeastern University in the program Applied Machine Intelligence
Kristu Jayanti College | Bangalore, India | 2019 - 2022
Successfully completed Bachelor degree in Computer Science with a strong GPA in the year 2022
St. Philomena's Public School & Junior College | 2017 - 2019
Successfully completed high school in the concentration Computer Science
Slay | June 2025 – December 2025
Northeastern University | January 2025 – June 2025
National Instruments | January 2022 – August 2024
IIIT Bangalore - Advanced Certification Program for Data Science
LinkedIn Learning - Azure Administration Essential Training
AWS Academy Cloud Foundations - Comprehensive cloud computing fundamentals
Design and implement robust data pipelines, ETL processes, and data warehouse solutions to transform raw data into actionable insights.
Develop intelligent systems using machine learning algorithms, neural networks, and AI frameworks to solve complex business problems.
Create custom software solutions, drivers, and applications using Python, C++, Java, and LabVIEW for various industries.
Perform comprehensive data analysis, statistical modeling, and create interactive dashboards for data-driven decision making.
Design and deploy scalable cloud-based solutions using AWS, Azure, and GCP for optimal performance and reliability.
Develop and customize ServiceNow solutions for IT Service Management, workflow automation, and enterprise integrations.
Asynchronous microservices pipeline using FastAPI and Redis for speech recognition (Whisper) and sentiment analysis (RoBERTa), with Docker containerization and horizontal scaling.
End-to-end MLOps platform for multi-label pathology detection on NIH ChestX-ray14 (~112K images) using DenseNet-121, Azure ML, Terraform IaC, and blue-green canary deployment with GradCAM/SHAP explainability.
Multi-agent AI platform built on LangGraph where specialized agents collaboratively analyze research papers, with FAISS-powered semantic search and arXiv integration.
Fault-tolerant distributed scheduling system with leader election, heartbeat monitoring, crash recovery, and Prometheus/Grafana observability.
Production ML system with fine-tuned DistilBERT for sentiment classification, LangGraph-based intelligent agent routing, and SageMaker deployment.
Cloud-native ETL pipeline processing FDA drug approvals and clinical trial data using Airflow DAGs, KubernetesPodOperator, AWS S3 data lake, and GitHub Actions CI/CD.
RAG-based conversational agent for automating academic advisor workflows, built with LangChain, Supabase vector databases, and real-time query processing.
Real-time AR application with MediaPipe Face Mesh, Three.js, achieving 30+ FPS on web and mobile platforms.
AI-powered desktop monitoring with facial recognition, emotion detection, and real-time productivity analytics dashboard.
Full-stack responsive web app for gaming community with Firebase real-time sync and authentication.
Interactive visualizations and comprehensive data analysis for loan application patterns and insights.
End-to-end DuckDB ETL pipeline processing 30GB+ transactional data, with A/B test simulation validating a 4.12% LTV lift, Streamlit visualization, and Power BI executive dashboards.
AI-powered multimodal coaching platform with NestJS microservices, MediaPipe video analysis, multi-provider LLM router, RAG with pgvector, and Expo mobile app with offline-first architecture.
End-to-end recommendation engine with CLIP embeddings, XGBoost Learning-to-Rank, PySpark data processing, Redis feature store, Airflow orchestration, and Evidently AI drift monitoring.
Boston, MA, USA
+1 617-934-9015
jaimes.a@northeastern.edu