i Joyce Chidiadi
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About Me

Hello, I am Joyce. With 8+ years experience in Software Engineering across DevOps, Machine Learning, and MLOps. I specialize in designing production-grade ML systems and embedding AI into real-world applications.

I enjoy building scalable ML and LLM pipelines, integrating observability, and automating the full lifecycle of model deployment. I am passionate about delivering secure, private AI solutions that bring measurable business value.

I also work with these Languages and Technologies:

  • Python
  • Java
  • Html/CSS
  • Bootstrap
  • GitHub Actions
  • Spark
  • Docker
  • Machine Learning
  • Deep Learning
  • AWS Sagemaker
  • GCP
  • MLOPS
  • SQL
  • Git
  • Tensorflow
  • PyTorch
  • Prometheus/Grafana/New Relic

What I Do

Machine Learning

I massage data and design intelligent systems.

I build and deploy ML, Deep Learning, and Generative AI solutions using tools like SageMaker, MLflow, and LangChain. My work includes implementing recommendation engines, LLM-powered chatbots, and predictive analytics pipelines in production environments.

MLOps

I design end-to-end MLOps workflows that automate data ingestion, model training, deployment, and monitoring.

My experience includes building pipelines with Airflow, MLflow, and Kubernetes, and deploying models securely in AWS and GCP.

I integrate observability with Prometheus, Grafana, and ElasticSearch to ensure reliable operations.

Software Engineering - GenAI

I engineer production-grade Generative AI solutions combining LLMs, vector databases, and scalable APIs.

My projects include Retrieval-Augmented Generation systems, document intelligence platforms, and secure ML services.

Work Experience

Senior Software Developer | ML - MLOps & GenAI Platform Royal Bank of Canada (RBC)

2022 – Present

- Led the architecture and delivery of production Retrieval-Augmented Generation (RAG) pipelines using LangChain and pgvector across AWS and Azure, supporting over 15 internal AI applications.

- Developed scalable ML workflows with Airflow, MLflow, Docker, and Kubernetes, reducing model retraining time by 40% and improving deployment frequency by 3x.

- Built and optimized GPT-3/4 and BERT-powered services for text summarization and classification, increasing content processing throughput by 60%.

- Established CI/CD pipelines with GitHub Actions and MLflow, achieving 100% reproducible deployments and traceable model lineage.

- Mentored a cross-functional team of 5 engineers, driving adoption of MLOps best practices and stakeholder collaborations.

Software Designer | ML Applications Engineer Hexagon Autonomy & Positioning, Calgary

December 2017 – 2022

- Designed and productionized a deep learning model for GNSS signal interference detection using CNNs on GPUs, improving detection accuracy by 35% compared to legacy systems.

- Granted US Patent #11988753 for GNSS-receiver interference detection using deep learning.

- Delivered observability dashboards with Prometheus, Grafana, ElasticSearch, and MySQL, reducing incident resolution times by 60%.

- Led Python codebase modernization and maintained comprehensive technical documentation for multiple products.

- Reviewed and merged 300+ pull requests, driving continuous improvement and mentoring junior developers.

Machine Learning / Software Engineering Consultant Upwork - Part Time

2020 – Present

- Designed and delivered end-to-end ML solutions for over 12 clients in e-commerce, real estate, and fintech, driving business KPIs such as lead conversion and customer retention.

- Built and deployed recommendation engines and predictive analytics pipelines using AWS SageMaker, Airflow, and Kubernetes, reducing time-to-insight by 50%.

- Developed Retrieval-Augmented Generation systems leveraging OpenAI APIs, LangChain, and pgvector to power custom search and conversational AI experiences.

- Implemented monitoring and observability frameworks with Prometheus and Grafana to track model drift, latency, and performance metrics.

- Created reusable infrastructure-as-code templates and CI/CD workflows to accelerate project delivery and ensure reproducibility.

Software Developer Stream Systems, Calgary

2017

- Developed Python and Java components to extend simulation platform capabilities and improve system reliability.

- Automated testing workflows using Selenium and Jenkins, reducing regression cycles by 40% and increasing release confidence.

- Built and maintained CI pipelines to streamline code integration and deployment processes.

- Diagnosed and resolved software defects, contributing to higher-quality releases and reduced support incidents.

Education

M.Sc. in Computer Science Georgia Institute of Technology

In-Progress

Interactive Intelligent Systems specialization with coursework in AI, Machine Learning, Health Informatics, and Algorithm Design.

M.Sc. in Petroleum Geoscience Institute of Petroleum Studies, University of Port Harcourt
B.Tech. in Geology Federal University of Technology, Owerri

Projects

sage_maker
Deploy ML App with AWS S3
An LSTM-based Sentiment Analysis of movie reviews deployed into production on AWS using SageMaker, EC2, and Lambda.
know_canada
Know Canada
An app for people to learn about Canada and prepare for their Canadian citizenship test.
recom_sys
Recommendation Systems
A system to improve IBM Watson user article recommendations.

Blog

churn_rate
Predicting Churn Rate with PySpark
A model for predicting customer churn with Random Forest, Logistic Regression, and Gradient Boosting Trees in PySpark on IBM Cloud.
raspberry_pi
Federated Learning with Raspberry Pi
Equipment setup for federated learning of a recurrent neural network on Raspberry Pis and virtual workers.
learn_python
Easiest Way to Learn Python
... don't give up.