Senior Machine Learning Engineer

Boardroom Appointments; Johannesburg; On-site; ZAR 1,000,000 - 1,400,000; Full time

Job summary

Boardroom Appointments is seeking a Senior Machine Learning Engineer to design, develop, and optimize ML models for real-world business applications. You will build scalable pipelines on cloud platforms, deploy models to production, and monitor performance with a focus on reproducibility.

The role requires 5+ years in ML/DL, strong Python and library experience, and collaboration with cross-functional teams to align ML solutions with business goals.

Qualifications

• Advanced degree in CS/DS/ML or related field.
• 5+ years in ML, DL, and production deployment.
• Strong Python and ML library experience.
• Hands-on cloud and MLOps tooling experience.
• Experience with big data stacks and ML pipelines.

Responsibilities

• Design, develop, and optimize ML models.
• Build scalable ML pipelines on cloud platforms.
• Collaborate with data engineers for data prep.
• Deploy models to production and monitor performance.
• Improve models via A/B testing and retraining.
• MLOps practices and automation for reproducibility.
• Monitor drift, reliability, and performance in production.
• Communicate findings to non-technical stakeholders and document processes.

Skills

Python

TensorFlow

PyTorch

Scikit-learn

Pandas

NumPy

AWS

GCP

Azure

Kubeflow

MLflow

SageMaker

Spark

SQL

NoSQL

Git

Docker

Kubernetes

CNNs

RNNs

Transformers

NLP

Computer Vision

Reinforcement Learning

AutoML

Hyperparameter Tuning

Edge AI

Education

Bachelor's degree

Master's degree

PhD (plus)

Tools

Kubeflow

MLflow

SageMaker

Job description

About the job Senior Machine Learning Engineer

Key Responsibilities:

• Model Development & Optimization: Design, develop, and optimize machine learning models for real-world applications, ensuring high accuracy, scalability, and efficiency.
• ML Pipeline & Deployment: Build and maintain scalable ML pipelines using cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).

Feature Engineering & Data Processing: Collaborate with data engineers to preprocess, clean, and transform large datasets for training and inference.

Productionization: Deploy ML models into production, monitor performance, and continuously improve them through A/B testing and retraining.

Collaboration: Work closely with cross-functional teams including software engineers, product managers, and business stakeholders to align ML solutions with business objectives.

MLOps & Automation: Implement MLOps best practices, automate model training and deployment, and ensure reproducibility.

Performance Monitoring: Develop and maintain monitoring tools to track model performance, drift, and reliability in production.

Research & Innovation: Stay updated with the latest trends and advancements in AI/ML, and integrate cutting‑edge research into business solutions.

Required Qualifications & Skills:

Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus.

Experience: Minimum 5+ years of experience in machine learning, deep learning, and AI model deployment in production environments.

Programming: Strong proficiency in Python, with experience in libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.

Cloud & Infrastructure: Hands‑on experience with cloud services (AWS, GCP, Azure) and MLOps tools like Kubeflow, MLflow, or SageMaker.

Big Data & Databases: Experience with Spark, Hadoop, SQL, and NoSQL databases for handling large-scale datasets.

DevOps & CI/CD: Familiarity with Git, Docker, Kubernetes, and CI/CD pipelines for ML model deployment.

Algorithm Development: Strong knowledge of ML algorithms, deep learning architectures (CNNs, RNNs, Transformers), and optimization techniques.

Problem‑Solving: Strong analytical and problem‑solving skills with the ability to design innovative ML solutions for complex business challenges.

Excellent Communication: Ability to explain technical concepts to non-technical stakeholders and document ML processes effectively.

Preferred Qualifications:

Experience with NLP, Computer Vision, or Reinforcement Learning.

Hands‑on experience with AutoML, hyperparameter tuning, and model interpretability.

Experience with real-time ML applications and edge AI.

Contributions to open‑source ML frameworks or research publications.