Senior Machine Learning Engineer

Requisition Details & Talent Acquisition Specialist

REQ 138363 - Keabetswe Modise
Closing Date: 13 March 2025

Job Family

Information Technology

Career Stream

Application Development

Leadership Pipeline

Manage Self Expert

Job Purpose

Lead the design, development, and implementation of cutting-edge analytic engines and services, leveraging extensive experience and expertise in machine learning to develop and deploy scalable models, optimize algorithms, and drive data-driven decision-making.

Job Responsibilities

• Develop and maintain a machine learning platform, ensuring it meets the needs of the community and stakeholders.
• Design and build robust inference systems, such as APIs, batch processing, and real-time streaming, to facilitate the deployment and utilization of machine learning models.
• Implement MLOps practices to streamline the deployment, monitoring, and management of machine learning models in production.
• Automate the end-to-end machine learning pipeline, from data ingestion to model deployment and monitoring.
• Ensure the scalability and reliability of the machine learning platform, addressing performance bottlenecks and optimizing resource usage.
• Utilize big data technologies such as Spark, Ray, and Dask to handle large-scale data processing and distributed computing.
• Leverage GPU acceleration to enhance the performance and efficiency of machine learning models, particularly for deep learning tasks.
• Collaborate with cross-functional teams to integrate machine learning solutions into existing systems and workflows.
• Contribute to the development and maintenance of documentation, tutorials, and guides for the machine learning platform.
• Engage with the data science community, participating in discussions, code reviews, and contributions to foster collaboration and innovation.
• Spearheaded best-in-class statistical models and algorithms, building upon previous experiences and learnings.
• Conduct in-depth statistical analysis to extract valuable insights and patterns from complex datasets, contributing to data-driven decision-making.
• Offer actionable insights and advice to stakeholders, utilizing a solid foundation in AI/ML and contributing to the team's expertise.
• Contribute to the creation of value from enterprise-wide data, assisting in the translation of data into meaningful business solutions.
• Experienced in deploying or contributed to deployment of at least one end-to-end data science solution that has yielded significant value in the organization at an enterprise level.
• Contribute to the shaping of the organization's AI/ML strategy, aligning it with evolving business needs.
• Assist in transforming data science prototypes into scalable machine learning solutions for deployment.
• Collaborate with experienced team members to design dynamic ML models and systems, incorporating the capability for adaptability and retraining.
• Participate in periodic evaluations of ML systems, ensuring they align with corporate and IT strategies.
• Expert proficiency in programming tools (such as Python, R, etc.) for data manipulation, statistical analysis, and machine learning tasks is essential.
• Demonstrate a profound command over computer science fundamentals, encompassing expert-level knowledge of data structures, algorithms, computability and complexity, and computer architecture.
• Utilize machine learning algorithms and libraries effectively, following established best practices and guidelines.
• Communicate technical concepts effectively to diverse audiences, adapting explanations for non-programming experts.
• Stay informed about the latest tools and techniques, engaging in continuous learning to enhance skills and knowledge.
• Proficiency in cloud computing and hands-on experience with deploying complex data science projects on cloud platforms.
• Collaborate with the team, sharing ideas and insights while conducting experiments and researching best practices.
• Seek opportunities for personal growth and development, actively participating in knowledge-sharing and mentorship.
• Contribute to the achievement of the business strategy, objectives, and values, contributing to the organization's success.

Essential Qualifications - NQF Level

Preferred Qualification

• STEM Qualification
• Engineering, Computer Science, Econometrics, Mathematical Statistics, Actuary Science
• Masters or Doctorate will be an added advantage

Preferred Certifications

• Cloud (Azure, AWS), DEVOPS or Data engineering certification.
• Any Data Science certification will be an added advantage, Coursera, Udemy, SAS Data Scientist certification, Microsoft Data Scientist.

Minimum Experience Level

• MS/PhD in STEM or related technical discipline
• 7 years’ plus  experience in a data science or software engineering role.
• Deep knowledge of machine learning, statistics, optimization or related field.
• Knowledge of Graph Database technology will be a major advantage
• Excellent written and verbal communication skills along with strong desire to work in cross functional teams.
• Attitude to thrive in a fun, fast-paced start-up like environment      

Technical / Professional Knowledge

• Data Mining
• Data analysis
• Statistical Analysis
• Supervised Learning
• Big Data Technologies
• Unsupervised Learning
• NLP
• Deep Learning
• Feature Engineering/Selection
• HyperParameter Tuning
• Programming
• Model Deployment/Monitoring
• MLOps
• API Development
• Inference Systems
• GPU Utilization
• Distributed Computing (Spark, Ray, Dask)
• Automation of ML Pipelines
• Scalability and Reliability Engineering
• Cloud Computing
• Documentation and Tutorials Development
• Graph Databases
• PostgreSQL, Redis

Behavioural Competencies

• Decision Making
• Innovation
• Technical/Professional Knowledge and Skills
• Customer Focus
• Applied Learning
• Improvement Continuous Improvement