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Nellie Cordova

About me

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Grounded in production. Driven by results.

Hi—I'm Nellie.

ML systems and applied AI — built with engineering discipline.

I build ML systems and applied AI pipelines — RAG frameworks, multimodal models, and agentic workflows — with the engineering discipline that comes from production software development. My work combines graduate-level ML depth with a focus on systems that are evaluatable, testable, and built to last.

Recent projects include a modular RAG framework with hybrid retrieval and a built-in evaluation harness, a two-stage multimodal pipeline mapping food images to generated recipes, and an agentic job post extraction system with a no-hallucination policy. I approach ML work the way I approach any engineering problem: define the metric, establish a baseline, then improve deliberately.

Earlier in my career I shipped backend microservices on JPMorgan's real-time payments platform — test automation, CI quality gates, regulated releases. That foundation shapes how I work on ML today: reproducible pipelines, clear interfaces, and the discipline to know when a system is actually ready.

I'm targeting applied ML and modeling-heavy data science roles, and I'm also open to ML-adjacent backend teams where experimentation and production live side by side.

Specialization ML & AI Systems
Certification AWS AI Practitioner
Education M.S. CS (ML), Georgia Tech — in progress
Languages English · Spanish
Seeking ML Engineer · AI Engineer roles
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Resume

Profile

Professional Summary

Applied machine learning practitioner with a strong software engineering foundation and production experience on a real-time payments platform. Currently completing an M.S. in Computer Science (Machine Learning).

Experienced in end-to-end ML/LLM projects, including modeling, offline evaluation, error analysis, and API-based deployment, with a disciplined approach to testing, CI, and release readiness.


Information


Technical Skills

  • Languages — Python (primary) · Java · SQL · R · C++ (academic)

  • Backend & APIs — FastAPI · REST APIs · Pydantic · typed request/response models

  • Quality & Delivery — CI/CD (Jenkins, Maven) · automated testing (JUnit/Cucumber) · API/perf testing (Postman, SOAP UI, JMeter) · release readiness

  • Cloud & Ops — Docker · AWS (AI Practitioner) · monitoring/alerting · runbooks

  • LLM / RAG / Agents — RAG pipelines (retrieval, reranking, citations) · evaluation & guardrails · prompt/tool workflows · OpenAI Agents SDK · LangChain (exploratory)

  • ML Foundations — PyTorch · scikit-learn · Transformers · NumPy · pandas · classification · fine-tuning & transfer learning · sequence models (RNN/LSTM, seq2seq) · attention / memory networks

  • Data & Evaluation — experiment design · cross-validation · data preprocessing/augmentation · metrics (F1, RMSE, perplexity) · error analysis · visualization (Matplotlib)

  • Developer Tools — Git · Jupyter · VS Code · IntelliJ · JIRA · Bitbucket · Confluence

  • Other — English & Spanish (bilingual)

Professional Experience

Software Engineer

JPMorgan Chase & Co. | Tampa, FL

2019 - 2021

Real-Time Payments Platform

  • Contributed end-to-end across the SDLC for Java-based payments microservices and drove production readiness.
  • Authored comprehensive operational runbooks—architecture, dependencies/configs, failure modes, diagnostic checklists, and step-by-step remediation—to enable fast triage, recovery, and self-serve on-call; provided light support on monitoring/alerting dashboards with platform/SRE.
  • Built and maintained test automation (JUnit, Cucumber); enforced CI quality gates (tests, static analysis, coverage) to keep merges green, and executed functional/perf testing (Postman, SOAP UI, JMeter).
  • Managed regulated releases: compiled release evidence (test/coverage reports, change tickets, approvals) to secure production sign-off; presented services to global production management.
  • Led/participated in agile rituals (sprint planning, backlog refinement); authored technical docs; coordinated across time zones.
2019 - 2020

Salesforce Platform

  • Delivered Salesforce CRM data-collection and reporting features for a social-impact organization.
  • Gathered stakeholder requirements and translated them into schema updates, UI changes, and documented technical implementations.
  • Expanded the underlying data model by adding new objects, fields, and relationships, and updated the custom Visualforce UI to align with the revised schema for accurate structured data capture.
  • Contributed a small Flow-based automation to digitize an existing paper survey, enabling standardized survey generation within the CRM.

ML Research Assistant

CS Dept. @ William Paterson University | Wayne, NJ

2018 - 2019
  • Performed exploratory data analysis on behavioral survey data to complement hypothesis-driven research on teen tanning behaviors.
  • Cleaned and prepared structured survey data, handling missing values and categorical variables.
  • Built and evaluated predictive and unsupervised models in Python and R, including Logistic Regression, KNN, and clustering methods.
  • Compared model behavior and results to understand key drivers, limitations, and trade-offs.
  • Communicated findings and insights to faculty and student audiences through presentations.

Education

M.S. in Computer Science (Machine Learning)

Georgia Institute of Technology | Atlanta, GA

2022 - Present

Relevant Coursework: Machine Learning, Deep Learning, Natural Language Processing, Reinforcement Learning, Network Science

B.A. in Mathematics | Minor: Computer Science

William Paterson University | Wayne, NJ

2016 - 2019

Relevant Coursework: Applied Regression Analysis, Data Warehouse & Data Mining, Database Management Systems, Cloud Computing


Magna Cum Laude • Pi Mu Epsilon (National Mathematics Honor Society)

Certifications

AWS Certified AI Practitioner

2025 - 2028

Group 2 Social / Behavioral Research Investigators and Key Personnel

CITI Program

2024 - 2027
SWE Resume ML Resume

SWE Preview ML Preview

Projects

  • All Projects
  • AI
  • DL
  • NLP
  • UX
  • Backend / Systems
Modular RAG

Modular RAG

Flow-based RAG framework — hybrid retrieval (BM25 + FAISS, RRF fusion, MMR reranking), explicit wiring, vendor isolation, observability-first architecture.

Retrieval evaluation framework included (Hits@k, MRR, NDCG).


FastAPI Hybrid retrieval FAISS LLM
Plate2Recipe

Plate2Recipe – Food Image to Recipe Generation

Two-stage multimodal pipeline: ViT ingredient recognition → GPT-2/LSTM recipe generation.

Key finding: lower training loss (100k samples) produced worse outputs than a smaller, better-tuned run (10k samples) — quality ≠ loss.


PyTorch ViT GPT-2 LSTM
RAG v1

RAG System with Guardrails

RAG v1 — BM25 + FAISS hybrid retrieval, RRF + MMR, citation-aware answering. Led to Modular RAG architecture.


FastAPI LLM FAISS
CRM & Ticketing System

CRM & Ticketing System

FastAPI backend with Docker, RBAC, and a deployed HuggingFace Spaces demo.


FastAPI Docker RBAC Gradio UI
FoodDB — Local Nutrition Facts Database

FoodDB

Local-first nutrition facts catalogue — SQLite backend, Typer CLI, and Streamlit UI sharing one service layer. Regex parser + swappable OCR backends (Tesseract / Ollama vision).


Python SQLite Streamlit OCR
ProductLens

ProductLens

LLM-driven product comparison tool that ranks products based on user priorities.


GenAI LLM OpenAI Agents SDK Gradio UI
Professional TwinBot AI

Professional TwinBot AI

AI-powered resume-grounded assistant: tool-style prompting + retrieval patterns for accurate, personalized responses.


GenAI LLM Chatbot Gradio UI
Memory-augmented QA System

Memory-augmented QA System

Attention-based QA over structured knowledge using memory networks.


PyTorch NumPy Tokenization attention/memory networks
Notification System Redesign

Notification System Redesign

User-centered redesign of Discord's notification experience.


UX Research Prototyping Usability Testing

Let's Connect!

Looking for ML Engineer and AI Engineer roles.


I build RAG pipelines, agentic workflows, and applied ML systems — with production engineering discipline behind them.
Recruiters: a short note + role link is perfect.


Contact

Open to roles