Machine Learning Engineer

Full Time
  • Full Time
  • TBC
  • Salary: R35 000 - R 70 000 ( depending on knowledge and experience )

Machine Learning Engineer – Job Description 

The Machine Learning Engineer is primarily responsible for building end-to-end machine learning models from ideation to deployment and scalability. In addition, the ML engineer is responsible for building operational frameworks for ensuring good governance, reproducible and templatized frameworks with ML-Ops architectures.  The ML Engineer creates new and improved data driven solutions to assist the organization and its clients in answering business questions, gaining competitive advantage, identifying new market opportunities, increasing efficiencies and/or reducing costs.

Duties & Responsibilities

  • Deploying ML-Ops pipelines and orchestration engines to scale model training and deployment.
  • Integration of ML pipelines into data warehouses and source environments to extract relevant data fields.
  • Robust feature engineering and large scale feature store creation.
  • Deploying repeatable pipeline templates, frameworks and re-usable design patterns.
  • Deploying models using cloud native, open source and fully managed services.
  • Implement best practice model monitoring (real-time reporting for model drift, performance, retraining and management).
  • Modelling complex business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualization techniques.
  • Overseeing data mining techniques and conduct statistical analysis to large, structured, and unstructured data sets to understand and analyse phenomena.
  • Create machine learning models to predict health risks and personalise recommendations.
  • Work in a cross-functional team, collaborating with data scientists, engineers, and analysts to understand project goals, interpret end-user’s intent and drive the build, implementation and scale-up of algorithms for measurable impact.
  • Research and implement appropriate machine learning algorithms and tools by selecting the correct libraries, programming languages and frameworks for each task.
  • Understand and use computer science fundamentals, including data structures, algorithms, computability, complexity, and computer architecture.

Desired Experience & Qualification

  • Advanced degree in computer science, math, statistics or a related discipline.
  • 5+ years Data Engineering / ML Engineering experience, with extensive data modelling and data architecture skills.
  • 2+ years experience deploying models using best practice ML-Ops approaches (CI/CD, monitoring, governance, scalable serving, etc.), on various open source and cloud native technologies (Kubeflow, Google Cloud AI Platform, AWS Sagemaker, etc.).
  • Deep knowledge of machine learning, statistics, optimization or related fields.
  • Background in machine learning frameworks such as TensorFlow or Keras.
  • Experience working with large data sets, simulation/optimization and distributed computing tools.
  • Exceptional data management skills and the ability to perform complex modeling on dynamic data sets.
  • Advanced math skills (linear algebra, Bayesian statistics, group theory).
  • Hands on experience with: AWS / Azure, Python, SQL, C# /Java, Spark.
  • Experience with data mining techniques and statistical analysis.
  • Experience working with large structured/ unstructured data sets.

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