Charlotte ***

CAREER SUMMARY

Senior Machine Learning Engineer with 6 years of experience designing, developing and deploying machine learning solutions across production environments. Experienced in the full machine learning lifecycle, including data preparation, feature engineering, model development, evaluation, deployment, monitoring and continuous improvement. Strong background in supervised and unsupervised learning, deep learning, natural language processing and predictive modelling. Experienced in building scalable ML pipelines and production-grade machine learning systems using Python, SQL, cloud platforms and modern MLOps practices. Strong ability to collaborate with data scientists, software engineers, product managers and business stakeholders to translate complex business problems into practical machine learning solutions. Passionate about developing reliable, scalable and measurable AI systems that deliver meaningful business outcomes.

EDUCATION

University of Queensland, Brisbane QLD
Master of Data Science, February 2016 – December 2017

EXPERIENCE

AI & Data Solutions Australia, Brisbane QLD — Senior Machine Learning Engineer, January 2022 – Current

Lead the design, development and deployment of machine learning solutions for production applications across a range of business use cases.
Work closely with data scientists, software engineers, product managers and business stakeholders to define machine learning requirements and translate business problems into technical solutions.
Design end-to-end machine learning pipelines covering data preparation, feature engineering, model training, evaluation, deployment and monitoring.
Develop and optimise machine learning models for classification, regression, forecasting, recommendation and predictive analytics use cases.
Build scalable data and ML pipelines using Python, SQL, cloud services and distributed processing technologies.
Develop production-ready machine learning services and APIs that integrate models into existing software applications and business platforms.
Implement model evaluation frameworks and establish appropriate performance metrics to assess model accuracy, reliability and business impact.
Apply machine learning techniques including gradient boosting, random forests, neural networks, clustering and deep learning models.
Develop and maintain MLOps workflows covering model versioning, automated training, deployment, monitoring and model lifecycle management.
Monitor production models to identify performance degradation, data drift and operational issues, implementing improvements as required.
Collaborate with software engineering teams to improve model serving, application integration, scalability and system performance.
Conduct technical investigations and experiments to evaluate new machine learning approaches, frameworks and technologies.
Provide technical guidance and mentoring to junior machine learning engineers and data scientists.
Present technical findings, model performance and recommendations to senior technical and business stakeholders.

Data Analytics Technologies, Brisbane QLD — Machine Learning Engineer, January 2020 – December 2021

Developed machine learning models and predictive analytics solutions to support business and operational decision-making.
Performed data cleaning, transformation, feature engineering and exploratory data analysis using Python and SQL.
Trained, evaluated and optimised machine learning models using structured and unstructured datasets.
Developed predictive models for classification, regression, forecasting and customer behaviour analysis.
Built reusable data processing and machine learning pipelines to improve development efficiency and model reproducibility.
Conducted model validation and hyperparameter optimisation using appropriate evaluation techniques and performance metrics.
Worked with data scientists and software engineers to transition machine learning models from experimentation into production environments.
Developed automated scripts and workflows for data processing, model training and evaluation.
Used cloud-based machine learning and data services to support scalable model development and deployment.
Investigated model performance issues and implemented improvements to data quality, feature engineering and model architecture.
Documented machine learning processes, model assumptions, experiments, results and technical decisions.
Participated in Agile development processes including sprint planning, technical discussions, code reviews and retrospectives.
TECHNICAL SKILLS

Machine Learning: Supervised learning, unsupervised learning, classification, regression, clustering, recommendation systems, predictive modelling, feature engineering, model optimisation

Deep Learning & AI: Neural networks, deep learning, NLP, computer vision, PyTorch, TensorFlow, transformer models, embeddings

Programming: Python, SQL, R, Bash

Machine Learning Libraries: Scikit-learn, Pandas, NumPy, XGBoost, PyTorch, TensorFlow, Hugging Face

MLOps: ML pipelines, model deployment, model serving, model monitoring, experiment tracking, model versioning, CI/CD, automated training, data validation

Cloud & Infrastructure: AWS, Azure, Docker, Kubernetes, cloud-based ML services, distributed computing

Data Engineering: ETL pipelines, data preprocessing, data modelling, data quality, feature pipelines, Spark, Databricks

Databases: PostgreSQL, MySQL, SQL Server, MongoDB, data warehouses, SQL optimisation

Development Tools: Git, GitHub, GitLab, Jupyter, MLflow, Airflow, VS Code, Jira

Analytics & Visualisation: Statistical analysis, A/B testing, model evaluation, performance monitoring, Matplotlib, Power BI

Professional Skills: Technical leadership, problem-solving, analytical thinking, stakeholder communication, mentoring, technical documentation, project collaboration

Education

2016-02 to 2017-12
Master of Data Science
University of Queensland, Brisbane QLD

Skills

Machine Learning | Artificial Intelligence | Deep Learning | Predictive Modelling | Python | SQL | Scikit-learn | PyTorch | TensorFlow | XGBoost | Pandas | NumPy | NLP | Computer Vision | Feature Engineering | Model Development | Model Evaluation | Model Deployment | MLOps | ML Pipelines | Model Monitoring | MLflow | AWS | Azure | Docker | Kubernetes | Databricks | Apache Spark | Airflow | Data Engineering | ETL | Data Modelling | Statistical Analysis | Experimentation | CI/CD | REST APIs | Software Engineering | Technical Leadership | Agile | Stakeholder Management

Occupation: Senior Machine Learning Engineer

Experience: 6 years

Location: Queensland, Australia

Skills: Python, TensorFlow, Machine Learning

Experience: 6 years

Daily rate: AUD 500

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