163 AI Engineer Africa jobs in South Africa

About the Role
Our client is looking for a hands-on Data Science & Enablement Lead to execute the organisation’s data vision and priorities. This role focuses on operationalising Master Data Management (MDM), data governance, data quality, architecture, and enabling AI, advanced analytics, and data science. You will ensure that enterprise data is accurate, governed, and integrated to support business operations, compliance, and innovation. This includes collaborating across Architecture, Operations, Risk, Governance, and IT to build a scalable, science-ready data ecosystem, integrating data from systems such as Oracle E-Business Suite.

Key Responsibilities

1. Enterprise Data Execution
   • Execute enterprise data strategy aligned with transformation, compliance, and AI/data science goals.
   • Champion data standards, governance processes, and quality metrics across functions.
   • Lead governance forums and implement policies, standards, and procedures.
   • Develop and maintain an enterprise data platform using Microsoft Azure, Microsoft Fabric, or similar.
   • Enable business teams with structured, accessible, and trusted datasets.
2. Master Data Management (MDM)
   • Implement MDM framework across master data domains (items, customers, suppliers, etc.).
   • Harmonize master data for consistency across systems and support analytics/AI.
   • Monitor and improve data quality in terms of completeness, accuracy, and timeliness.
3. Data Integration & Platform Development
   • Design and operate a central data lake according to best practices.
   • Lead data integration from systems, including Oracle E-Business Suite and others.
   • Design and implement secure data access controls using Active Directory groups, ensuring authorised data access.
   • Ensure data is accessible, secure, classified, and ready for AI, analytics, and data science.
4. Governance, Stewardship & Quality
   • Implement data ownership, stewardship, and accountability models.
   • Support stakeholders in compliance with governance controls.
   • Monitor data quality metrics and address issues within SLAs.
   • Ensure data meets ethical standards and is suitable for automated and algorithmic use.
5. Cross-Functional Delivery
   • Collaborate with Operations, Supply Chain, and Data Science teams to deliver integrated, reliable data.
   • Work with Risk & Governance teams to maintain compliance, audit, and AI-risk policies.
   • Enable analytics and data science by supporting structured, interoperable data pipelines.

Key Deliverables

• Secure & Scalable Data Lake: Implement and maintain a centralized repository for business-critical data with AD/role-based access.
• Integrated Data Pipelines: Implement reliable and secure pipelines from Oracle and other platforms.
• MDM Framework: Deploy MDM for critical entities (customers, products, vendors) to ensure data accuracy and consistency.
• AI/ML Enablement: Support the design and deployment of ML models for predictive analytics, anomaly detection, segmentation.
• Data Access Architecture: Implement security frameworks based on Active Directory and role-based security.
• AI Infrastructure & Insights: Build infrastructure for model deployment, performance monitoring, and automated reporting.
• Team Collaboration: Lead efforts with business units to discover AI/data opportunities and refine models.

Qualifications & Experience

Essential

• 8–10+ years in data management, including MDM, governance, integration, and quality initiatives.
• Experience in the full development lifecycle of a Data Lake in a mid to large enterprise.
• Expertise in Oracle E-Business Suite and integrating data from multiple systems.
• Deep knowledge of MDM best practices and AI/ML model building.
• Experience with Microsoft Azure, Microsoft Fabric, or similar platforms.
• Strong project management skills for complex data initiatives.
• Proficiency in Python, SQL, Spark, cloud ETL, and orchestration tools.
• Experience in designing secure AD/role-based access control systems.
• Experience with streaming data integration tools like Apache Kafka or Azure Event Hubs.

Desirable

• Familiarity with data governance frameworks (e.g., DAMA, DCAM).
• Experience with data cataloging/metadata tools.
• Experience in regulated industries such as finance, healthcare, or manufacturing.
• Background in FMCG, manufacturing, retail, or other data-intensive sectors.

Education

• Bachelor's degree in IT, Computer Science, Data Science, or a related field.
• Postgraduate qualifications in data, analytics, or BI are advantageous.

Technical Competence

• Skilled in Azure Data Factory, Synapse, Purview, Microsoft Fabric, etc.
• Familiar with Oracle EBS data structures and integration approaches.
• Hands-on experience with MDM tools, ETL platforms, and governance frameworks.
• Strong knowledge of AI/data science needs: labeling, versioning, ethics.
• Proficient in operationalizing analytics pipelines for BI, AI, and ML.
• Experienced with AD-based security and role-based access control.
• Skilled in real-time data streaming solutions (e.g., Kafka, Event Hubs).

Key Competencies

• Strategic thinking and end-to-end solution ownership.
• Strong analytical and problem-solving capability.
• Excellent communication and stakeholder engagement skills.
• Leadership and mentorship abilities.
• Results-driven with a strong focus on quality and delivery.

We have an exciting opportunity for a Head of Advanced Data Science role in an intelligence solutions company. Our client provides services and technologies to help organizations collect, analyze, and utilize data to gain insights and make better decisions. They are looking to hire an experienced candidate who will play an important role in shaping the future of their data science and AI strategy. The successful candidate’s work will be the cornerstone of innovation, transforming complex data into actionable insights that drive significant business impact for clients across various industries. This is more than just a leadership position; it’s an opportunity to pioneer the next generation of predictive and prescriptive analytics solutions. Requirements: Qualifications: BCom/BSc Honors/MCom/MSc/PhD Statistics, Mathematics or computer science (must have proven machine learning building experience) Experience: 10 years or more relevant analytical experience with at least 3 in ML/AI Technical skills:

• Data Analysis: Exceptional ability to work with, analyse, interpret, and develop insights products on data.
• Machine Learning: Strong understanding and practical application of statistical concepts and machine learning techniques.
• Technical Proficiency: Expertise in Python, R & SQL required with experience in TensorFlow, Hugging Face, FastHTML, FastAPI, Flask and Django advantageous.
• Familiarity with Docker containers and predictive model deployment in Microsoft Azure or similar.
• Working with virtualised applications and programming environments.
• Adaptability: Ability to adjust to different programming environments based on the project needs.
• Professional approach to report writing, documentation and other written communications.
• Familiarity with Agile concepts and their application in a Data Science environment.

Job Purpose:

To support the business in identifying opportunities for different segments, and work to use identified insights to design compelling, disruptive and commercially viable Client Value Propositions for the company Insurance offerings.

Job Responsibilities:

• Provide input on activities for relevant CX reports by collecting information on status and results.
• Co-ordinate tasks, resources and internal and external stakeholders to support the implementation of strategic objectives.
• Meet financial objectives by co-ordinating activities in line with budget/target requirements.
• Design and Develop Client Value Proposition (CVP) by ensuring that the CVP’s resonate with the customers’ needs and while aligning with commercial imperatives.
• Identify and engage with all stakeholders to ensure implementation of CVP activities.
• Build and maintain collaborative relationships with stakeholders to deliver on CVP objectives.
• Understand customer needs and market trends.
• Design and develop market leading customer value propositions that are based on customer needs, behaviour, and potential to optimize both customer value proposition and customer experience across our target segments and population groups.
• Ensure that the CVPs resonate with the customers’ needs and also with commercial imperatives.
• Monitor, analyse and articulate in detail competitor activity and emerging market trends to inform the CVPs and ensure the designs remain competitive and up to date.
• Leverage Analytics, MI & Customer Experience to build a fact-based understanding of customer needs and use these insights to support or disprove particular areas of prioritization, focus and courses of action.
• Ensure alignment with the segment customer value proposition.
• Monitor and provide feedback to enhance customer value proposition.
• Contribute to Segment and Proposition strategy.
• Lead and ensure the implementation of the CVP from end to end.
• Generate concise business plans in support of the CVPs.
• Work collaboratively with downstream colleagues with responsibility for building elements of the CVP to a definitive timetable.
• Contribute to a culture conducive to the achievement of transformation goals by participating in the company’s culture building initiatives (e.g. staff surveys, etc.).
• Participate and support corporate social responsibility initiatives for the achievement of key business strategies.
• Identify and recommend opportunities to enhance processes, systems and policies and support implementation of new processes, policies and systems.
• Improve personal capability and stay abreast of developments in field of expertise by identifying training courses and career progression for self through input and feedback from managers.
• Ensure personal growth and enable effectiveness in performance of roles and responsibilities by ensuring all learning activities are completed, experience practiced and certification obtained and/or maintained within specified time frames.
• Ensure information is provided correctly to stakeholders by maintaining knowledge sharing with the team.

Essential Qualifications:

• Advanced Diplomas/National 1st Degrees

Preferred Qualification:

• Post Graduate Diploma: Marketing Management

Type of Exposure:

• Conducting root cause analysis
• Analysing situations or data that requires an in-depth evaluation of multiple factors
• Influencing stakeholders to obtain buy-in for concepts and ideas.
• Challenging the status quo with a view to improving the environment or people's understanding
• Communicating standards to others
• Working with a group to identify alternative solutions to a problem
• Interacting with diverse people

Minimum Experience:

• Overall 7 years + experience in Client Experience, Customer Value Propositions and Customer Insights.
• 5 years’ experience in developing, implementing and being accountable for crafting of CVP, take to market strategy and business plan.
• 6 years’ experience in roles within market insights, consumer research or product management.
• 4 years’ + experience in Insurance and/or financial service industry.

Technical / Professional Knowledge:

• Business terms and definitions
• Cluster specific operations
• Communication Strategies
• Governance, Risk and Controls
• Principles of financial management
• Principles of project management
• Research methodology
• Decision-making process
• Data and Business analysis
• Competitive Intelligence

Behavioural Competencies:

• Continuous Learning
• Customer Focus
• Work Standards
• Building partnerships
• Planning and Organizing
• Technical/Professional Knowledge and Skills

Melio is seeking a passionate Machine Learning Engineer to join our expanding team:

• Level: Junior or Intermediate
• Position: Full time
• Salary: Based on technical experience

As a fast-paced start-up, our day-to-day is never the same. We look for candidates who love to take up new challenges and have the flexibility to go above-and-beyond the call of duty.

That being said, this specific role is for a team member to work in the Machine Learning AI team as a Machine Learning Engineer.

The candidate will be working with clients on projects focused on delivering reliable data-powered software applications to production. The primary focus of the role includes designing and implementing data pipelines, building models (machine learning or not), and deploying models.

Melio believes in nurturing cross-functional capabilities in our team, so you will need to work closely with other technical roles, such as BI specialists, data scientists, DevOps engineers and other data and machine learning engineers either to observe or to assist.

This is a technical role, but due to the consulting nature of many of our projects, the candidate needs to be able to communicate effectively with both business and technical stakeholders.

The list below is for both machine learning engineer and data engineer, but we are not expecting you to perform as both an ML engineer and a data engineer. The idea is you should be sufficiently comfortable with either role to be aware and able to communicate and learn from your colleagues.

• Fluent with the following languages: Python, Spark, PySpark, SQL.
• Strong analytical skills and passion for solving data problems.
• Strong communications skills and comfortable presenting your own thoughts to technical and business stakeholders.
• Familiar with building training and inference pipelines for ML projects.
• Familiar with fundamental machine learning theory and building, tuning, selecting models.
• Familiar with MLOps principals.
• Some experience with at least one Cloud provider.
• Some experience working with test automation and tools.
• Implement and test data engineering and machine learning software.
• Build and test data engineering and machine learning pipelines for data analytics or machine learning solutions.
• Collaborate and share technical knowledge with team members and co-workers.
• Follow all best practices and procedures as established by the client or industry.
• Document designed solutions and implemented tools.
• Brainstorm new solutions to improve data and ML software development and deployment.
• Basic understanding of the cloud-native ecosystem and desire to learn and grow in this environment.

• Assist with setting up CI (Continuous Integration) and CD (Continuous Delivery) tools with the team.
• Monitor data/model metrics, and develop ways to improve application development and deployment.
• Maintain day-to-day management and administration of projects.

• Bachelor’s degree in Computer Science, Engineering, Software Engineering, Applied Mathematics, Statistics, or related field.
• 2+ years experience working with data science and data engineering. Previous experience with software development (e.g. Python, Java, Go).

Desired Skills

• Contribute and/or passionate about open source projects.
• Masters/PhD in Data Science, Machine Learning or AI.
• AWS certifications (or any other cloud).
• Interested in learning more about Cloud Native Computing Foundation technologies.

• Up-to-date on latest industry trends; able to articulate trends clearly and confidently.
• Able to interact with other team members via code and design documents.
• Good interpersonal skills and communication with all levels of management.
• Able to multitask, prioritize, and manage time efficiently.
• Curious and eager to learn about new technologies.
• Strong in critical thinking and problem-solving.

If you are interested in this position please email Merelda ( ) with the below information:

We are looking for a talented Machine Learning Engineer to join our team, responsible for developing and deploying machine learning models and algorithms that drive business growth and innovation. The successful candidate will have a strong background in machine learning, deep learning, and software engineering, with a proven track record of delivering high-quality machine learning models and algorithms. The Machine Learning Engineer will work closely with cross-functional teams, including data science, product, and engineering, to identify opportunities for machine learning-driven innovation and develop strategic plans to execute on these opportunities.

1. Design, develop, and deploy machine learning models and algorithms that drive business growth and innovation
2. Collaborate with data scientists to develop and implement machine learning models and algorithms
3. Work with software engineers to integrate machine learning models and algorithms into production-ready software applications
4. Develop and maintain large-scale machine learning systems, including data pipelines, model training, and model serving
5. Optimize machine learning models and algorithms for performance, scalability, and reliability
6. Stay up-to-date with the latest advancements in machine learning, deep learning, and AI, applying this knowledge to drive innovation and improvement in machine learning models and algorithms
7. Collaborate with product managers to develop product roadmaps and prioritize features and requirements
8. Develop and maintain relationships with key stakeholders, including business leaders, product managers, and engineering teams
9. Communicate complex machine learning concepts and results to non-technical stakeholders, including business leaders and product managers

1. Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field
2. 3+ years of experience in machine learning, deep learning, or software engineering, with a focus on machine learning model development and deployment
3. Strong background in machine learning, deep learning, and software engineering, with expertise in areas such as natural language processing, computer vision, or recommender systems
4. Experience with machine learning frameworks and tools, such as TensorFlow, PyTorch, or Scikit-learn
5. Strong programming skills in languages such as Python, Java, or C++
6. Experience with cloud-based technologies, such as AWS or Google Cloud
7. Strong understanding of software engineering principles, including design patterns, testing, and version control
8. Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams

Technical Skills:

1. Machine learning frameworks: TensorFlow, PyTorch, Scikit-learn, etc.
2. Deep learning frameworks: Keras, TensorFlow, PyTorch, etc.
3. Cloud-based technologies: AWS, Google Cloud, Azure, etc.

Full time

Johannesburg

Sandton, South Africa | Posted on 08/28/2024

MUSE  is a consulting company, specialising in resourcing, recruitment and outsourcing of software development teams.

MUSE was founded and is run by experienced developers who are passionate about technology and innovation. We have a vision to be the best in the industry and to provide software development skills that are cutting-edge and high-quality.

We work with some of the leading companies in South Africa and we help them build software products and solutions that are game-changing and future-oriented. We are also at the forefront of applying AI, AR and Machine-Learning concepts to real-world problems.

Our main goal is to help our clients get the most value from their technology investments. We do this by understanding their needs and providing them with the best talent available. We aim to be a vital part of the SDLC.

Are you a talented and enthusiastic Machine Learning Engineer looking for an exciting opportunity? Join our Machine Learning Operations team and be at the forefront of designing, building, testing, deploying, and monitoring cutting-edge machine learning and analytics applications. This role offers the chance to work with state-of-the-art technologies and contribute to the automation of machine learning and AI use cases. Collaborate with data scientists, actuaries, data engineers, and other software engineers to help architect our bank’s modern Machine Learning ecosystem.

Machine Learning Automation and Software Engineering:

• Design, build, and deploy machine learning and analytics automation processes.
  
• Refactor existing code bases to enhance efficiency, robustness, scalability, and automation of machine learning workflows.
  

Cloud-Native Development:

• Utilize Databricks and Azure for data engineering and machine learning use cases.
  
• Leverage Azure services such as Azure Functions, CosmosDB, API Gateway, and Azure Machine Learning to build intelligent data applications.
  

DevOps and Software Engineering:

• Build CI/CD pipelines to improve development and deployment practices.
  
• Develop robust testing and monitoring capabilities for machine learning and AI use cases.
  
• Experience with Git, Jenkins, Azure DevOps, and Terraform is advantageous.
  
• Build APIs to serve machine learning models.
  
• Apply software engineering best practices to develop robust, scalable, and maintainable code.
  
• Create microservice applications using Docker and container orchestration tools like OpenShift.
  
• Collaborate with cross-functional teams to deliver high-quality software solutions for machine learning and data use cases.
  
• Create and maintain documentation of processes, technologies, and code bases.
  
• Familiarity with MLFlow, PyTorch, TensorFlow, etc., is beneficial for the productionization of machine learning use cases.
  
• Work closely with data scientists, actuaries, data engineers, and other software engineers to understand and address their data needs.
  
• Contribute actively to the architecting of our bank’s modern Machine Learning data ecosystem.
  

• 1-3 years of experience as a Software Engineer.
  
• Bachelor’s degree in engineering or a related field. Other qualifications will be considered if accompanied by sufficient experience in software engineering.
  

• 2 years of experience using Python and SQL.
  
• Exposure to Linux shell scripting is advantageous.
  
• Experience with Spark is advantageous.
  
• Interest in software architecture.
  
• Knowledge of cloud compute services.
  
• Familiarity with serverless computing and cloud-native development.
  
• Keen interest in systems design and software architecture.
  
• Knowledge of machine learning frameworks/packages (e.g., MLFlow, Spark ML, Sklearn).
  
• Understanding of CI/CD concepts and API development, with implementation experience being advantageous.
  
• Strong critical thinking, problem-solving, and collaboration skills.
  
• Ability to collaborate with cross-functional tech teams as well as business/product teams.
  
• Commitment to excellence and high-quality delivery.
  
• Passion for personal development and growth, with a high learning potential.
  

If you’re passionate about machine learning and eager to work in a dynamic and innovative environment, we’d love to hear from you! Apply now and be part of our journey to revolutionize the banking industry with cutting-edge AI and machine learning solutions.

We are looking for a talented Machine Learning Engineer to join our team, responsible for developing and deploying machine learning models and algorithms that drive business growth and innovation. The successful candidate will have a strong background in machine learning, deep learning, and software engineering, with a proven track record of delivering high-quality machine learning models and algorithms. The Machine Learning Engineer will work closely with cross-functional teams, including data science, product, and engineering, to identify opportunities for machine learning-driven innovation and develop strategic plans to execute on these opportunities.

1. Design, develop, and deploy machine learning models and algorithms that drive business growth and innovation
2. Collaborate with data scientists to develop and implement machine learning models and algorithms
3. Work with software engineers to integrate machine learning models and algorithms into production-ready software applications
4. Develop and maintain large-scale machine learning systems, including data pipelines, model training, and model serving
5. Optimize machine learning models and algorithms for performance, scalability, and reliability
6. Stay up-to-date with the latest advancements in machine learning, deep learning, and AI, applying this knowledge to drive innovation and improvement in machine learning models and algorithms
7. Collaborate with product managers to develop product roadmaps and prioritize features and requirements
8. Develop and maintain relationships with key stakeholders, including business leaders, product managers, and engineering teams
9. Communicate complex machine learning concepts and results to non-technical stakeholders, including business leaders and product managers

1. Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field
2. 3+ years of experience in machine learning, deep learning, or software engineering, with a focus on machine learning model development and deployment
3. Strong background in machine learning, deep learning, and software engineering, with expertise in areas such as natural language processing, computer vision, or recommender systems
4. Experience with machine learning frameworks and tools, such as TensorFlow, PyTorch, or Scikit-learn
5. Strong programming skills in languages such as Python, Java, or C++
6. Experience with cloud-based technologies, such as AWS or Google Cloud
7. Strong understanding of software engineering principles, including design patterns, testing, and version control
8. Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams

Technical Skills:

1. Machine learning frameworks: TensorFlow, PyTorch, Scikit-learn, etc.
2. Deep learning frameworks: Keras, TensorFlow, PyTorch, etc.
3. Cloud-based technologies: AWS, Google Cloud, Azure, etc.

Full time

Johannesburg

Sandton, South Africa | Posted on 08/28/2024

MUSE  is a consulting company, specialising in resourcing, recruitment and outsourcing of software development teams.

MUSE was founded and is run by experienced developers who are passionate about technology and innovation. We have a vision to be the best in the industry and to provide software development skills that are cutting-edge and high-quality.

We work with some of the leading companies in South Africa and we help them build software products and solutions that are game-changing and future-oriented. We are also at the forefront of applying AI, AR and Machine-Learning concepts to real-world problems.

Our main goal is to help our clients get the most value from their technology investments. We do this by understanding their needs and providing them with the best talent available. We aim to be a vital part of the SDLC.

Are you a talented and enthusiastic Machine Learning Engineer looking for an exciting opportunity? Join our Machine Learning Operations team and be at the forefront of designing, building, testing, deploying, and monitoring cutting-edge machine learning and analytics applications. This role offers the chance to work with state-of-the-art technologies and contribute to the automation of machine learning and AI use cases. Collaborate with data scientists, actuaries, data engineers, and other software engineers to help architect our bank’s modern Machine Learning ecosystem.

Machine Learning Automation and Software Engineering:

• Design, build, and deploy machine learning and analytics automation processes.
  
• Refactor existing code bases to enhance efficiency, robustness, scalability, and automation of machine learning workflows.
  

Cloud-Native Development:

• Utilize Databricks and Azure for data engineering and machine learning use cases.
  
• Leverage Azure services such as Azure Functions, CosmosDB, API Gateway, and Azure Machine Learning to build intelligent data applications.
  

DevOps and Software Engineering:

• Build CI/CD pipelines to improve development and deployment practices.
  
• Develop robust testing and monitoring capabilities for machine learning and AI use cases.
  
• Experience with Git, Jenkins, Azure DevOps, and Terraform is advantageous.
  
• Build APIs to serve machine learning models.
  
• Apply software engineering best practices to develop robust, scalable, and maintainable code.
  
• Create microservice applications using Docker and container orchestration tools like OpenShift.
  
• Collaborate with cross-functional teams to deliver high-quality software solutions for machine learning and data use cases.
  
• Create and maintain documentation of processes, technologies, and code bases.
  
• Familiarity with MLFlow, PyTorch, TensorFlow, etc., is beneficial for the productionization of machine learning use cases.
  
• Work closely with data scientists, actuaries, data engineers, and other software engineers to understand and address their data needs.
  
• Contribute actively to the architecting of our bank’s modern Machine Learning data ecosystem.
  

• 1-3 years of experience as a Software Engineer.
  
• Bachelor’s degree in engineering or a related field. Other qualifications will be considered if accompanied by sufficient experience in software engineering.
  

• 2 years of experience using Python and SQL.
  
• Exposure to Linux shell scripting is advantageous.
  
• Experience with Spark is advantageous.
  
• Interest in software architecture.
  
• Knowledge of cloud compute services.
  
• Familiarity with serverless computing and cloud-native development.
  
• Keen interest in systems design and software architecture.
  
• Knowledge of machine learning frameworks/packages (e.g., MLFlow, Spark ML, Sklearn).
  
• Understanding of CI/CD concepts and API development, with implementation experience being advantageous.
  
• Strong critical thinking, problem-solving, and collaboration skills.
  
• Ability to collaborate with cross-functional tech teams as well as business/product teams.
  
• Commitment to excellence and high-quality delivery.
  
• Passion for personal development and growth, with a high learning potential.
  

If you’re passionate about machine learning and eager to work in a dynamic and innovative environment, we’d love to hear from you! Apply now and be part of our journey to revolutionize the banking industry with cutting-edge AI and machine learning solutions.