45 Annotation Specialist jobs in South Africa

Help shape the future of AI We're seeking detail-oriented freelancers to join a global AI data annotation project. As an AI Data Annotation Specialist, you'll annotate, label, and curate data that powers advanced machine learning models. Your work—tagging images, labeling text, or collecting documents—will directly impact the accuracy and performance of AI systems used worldwide.

Key Responsibilities

• Annotate & Label: Tag, categorize, and review text, images, and documents.
• Collect Data: Gather and organize high-quality datasets for AI training.
• Ensure Quality: Review and validate data for precision and consistency.
• Collaborate Globally: Work with an international team to hit project milestones.

Flexible Hours

• Work up to 10 hours per day, 40 hours per week—on your schedule.

Qualification path

Basic Requirements

• Strong attention to detail and ability to follow precise instructions.
• Reliable internet connection and ability to work independently.
• Prior experience in data annotation or labeling is a plus but not required.
• Basic understanding of AI or machine learning concepts is advantageous.
• Strong proficiency in English for clear communication and reporting.

Assessment

To qualify, you will:

• Complete an initial qualification assessment to demonstrate suitability for the role.
• Provide ID verification for participation.

Payment

• Freelance rates range from $6–$18 USD per hour, depending on experience, geographic location, and project requirements.
• Payment is competitive and reflects the value of your contributions to AI training projects.

Once you have successfully applied and registered, please send a confirmation email to

with the subject line: Subject: Application Confirmation - (Job Title) via (Site) to ensure your application is processed. Please include the email address you used to register.

A specialised AI consulting firm is seeking a Machine Learning Engineer to join our team. This role offers the opportunity to work on high-impact AI initiatives within the banking sector , where you will play a key part in designing, deploying, and scaling advanced machine learning solutions. You’ll collaborate with data scientists, engineers, and business stakeholders to deliver end-to-end AI systems that enhance decision-making, improve customer experiences, and optimise critical processes. Beyond building models, you’ll be responsible for ensuring their performance, reliability, and scalability in production—using modern cloud platforms and MLOps practices.

Responsibilities:

• Design, build, and optimise machine learning models for banking applications.
• Implement scalable ML pipelines and integrate them into production environments.
• Collaborate with data scientists, data engineers, and business stakeholders to deliver end-to-end AI solutions.
• Deploy, monitor, and maintain ML models in AWS environments .
• Ensure model reliability, reproducibility, and performance throughout the lifecycle.
• Document methodologies, workflows, and best practices.

Qualifications and experience:

• 3–5 years of experience in machine learning, data science, or related field.
• Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with large datasets and SQL/NoSQL databases .
• Essential: Hands-on experience with AWS cloud services (SageMaker, S3, Lambda, EC2, Glue, Redshift).
• Familiarity with MLOps practices and CI/CD for ML pipelines.
• Strong problem-solving ability with a track record of translating business needs into technical solutions.
• Experience in banking or financial services is advantageous, but not required.
• Proven experience taking AI models into production at scale .
• Exposure to containerisation (Docker, Kubernetes).
• Familiarity with ML model monitoring and observability tools.

The reference number for this position is NG60802 which is a contract position in Johannesburg/Cape Town offering a contract rate of R550 to R750 per hour, salary negotiable based on experience. e-mail Nokuthula on or call her for a chat on to discuss this and other opportunities.

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• Exposure to the latest AI tools and frameworks (LangChain, Llama-Index, and Vector Databases).
• Opportunity to deploy real-world GenAI solutions that drive tangible business outcomes.
• Collaborative, forward-thinking environment where your input shapes strategy.
• Commitment to equity and inclusion African and Coloured candidates are strongly encouraged to apply.

• Design, implement, and optimize Generative AI solutions and RAG frameworks.
• Develop scalable data pipelines, perform ETL, and design dimensional models for optimal data storage.
• Deploy, monitor, and optimize AI models for performance, scalability, and cost efficiency.
• Collaborate with cross-functional teams to align solutions with business goals.
• Communicate complex AI concepts clearly to non-technical stakeholders.
• Stay ahead of the curve by researching and experimenting with new AI architectures and techniques.

• Education: Bachelors Degree in Computer Science, Data Science, Machine Learning, or a related field (Masters/PhD preferred).
• Experience: 3+ years in AI/ML, with at least 12 years specialising in Generative AI.
• Technical Skills:
  
  • Proficiency in Python, SQL, Pandas, and NumPy.
  • Experience with deep learning frameworks (TensorFlow and PyTorch) and LLM tools (LangChain and Llama-Index).
  • Cloud platforms (AWS, GCP, and Azure) and containerization (Docker and Kubernetes).
  • Strong data engineering skills (ETL, pipeline design, and data modelling).
  • Knowledge of MLOps practices for model versioning, monitoring, and retraining.
• Soft Skills: Proactive, curious, collaborative, and strong communicator.
• Preferred Certifications: AWS ML Specialty, GCP ML Engineer, Azure AI Engineer, or similar.

About talent match africa (tma)

talent match africa
is on a mission to unlock Africa's potential by connecting exceptional talent with organizations that are shaping the future. We don't just match skills to roles - we match people to possibilities. With a strong network across industries and a people-first approach, we help companies thrive while empowering professionals to build meaningful careers.

About this role

As a
Machine Learning Engineer
, you'll go beyond writing code to solving real-world problems with data. This role is about designing intelligent systems that learn and improve, tackling complex challenges that demand both creativity and technical depth. You'll thrive in collaboration, bringing ideas to life alongside a dynamic team.

What You'll Do

• Design, build, and deploy machine learning models and algorithms
• Develop scalable data pipelines for model training and inference
• Optimize models for performance and efficiency
• Collaborate with data scientists to bring research into production
• Monitor and maintain ML models in live environments
• Implement A/B testing frameworks to validate models
• Work with large-scale datasets and distributed computing systems
• Maintain and enhance existing ML systems

What We're Looking For

Required

• 3+ years of experience in machine learning and software development
• Proficiency in Python, R, or Scala
• Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Familiarity with cloud platforms (AWS, GCP, Azure)
• Strong understanding of statistics and mathematics
• Experience with version control and software engineering best practices

Preferred

• Exposure to MLOps tools and practices
• Knowledge of containerization (Docker, Kubernetes)
• Experience building real-time inference systems

Why tma?

At
talent match africa
, you're not just taking on a job, you're joining a movement. You'll work with innovative teams, get exposure to impactful projects, and be part of an ecosystem that's redefining how Africa connects to opportunities.

The role of the Specialist Machine Learning Engineer encompasses many activities, including (but not limited to):

• Focusing on niche areas of machine learning, such as natural language processing, computer vision, or reinforcement learning.
• Developing domain-specific ML models tailored to specialized business needs.
• Conducting in-depth research and prototyping innovative solutions using advanced ML techniques.
• Identifying opportunities to apply cutting-edge machine learning advancements to improve processes or create new capabilities.
• Collaborating with other Engineers to transfer research findings into scalable, production-ready solutions.
• Providing expert insights on specific tools, frameworks, or algorithms, ensuring the organization stays ahead in ML innovation.
• Contributing to the development of internal ML tools and libraries to streamline workflows.

Minimum Qualification:

• NQF 6 or higher tertiary qualification in Information Communication Technology (ICT) field incorporating (but not limited to) Information Systems; Cloud certification.

Minimum Experience:

• Minimum of 6 years' experience in a field of a Machine Learning Engineer role.

This 12-month remote contract role places you on an international machine learning project through Dijkstrack for one of our global technology partners. You'll work within a distributed engineering team to design, train, and deploy ML models using modern frameworks like PyTorch. Dijkstrack engineers enjoy access to technical community support, structured delivery processes, and the opportunity to embed within long-term global product teams.

• Design, train, and deploy machine learning and deep learning models using PyTorch
• Build and maintain ML pipelines, model training workflows, and data processing components
• Work with product and engineering teams to integrate models into production systems
• Analyse datasets, performance metrics, and optimise models for accuracy and efficiency
• Implement MLOps best practices for model versioning, deployment, and monitoring
• Document models, datasets, evaluations, and maintain reproducibility standards
• Participate in code reviews and uphold engineering quality in ML codebases

• Strong proficiency in Python for ML development
• Hands-on experience with PyTorch (TensorFlow experience also welcome)
• Solid understanding of machine learning fundamentals, model architectures, training loops
• Ability to work with large datasets, ETL pipelines, and model optimisation
• Familiarity with Git workflows, remote collaboration, and agile delivery models

• Experience with ML Ops tools like MLflow, Weights & Biases, SageMaker, or similar
• Exposure to cloud-based ML deployments (AWS, Azure, GCP)
• Knowledge of microservice integration for ML models
• Prior work in AI product teams or international ML research environments

• Join a network of engineers delivering advanced ML solutions internationally
• Technical community of senior engineers across data, backend, and product domains
• Remote-first culture, global exposure, and structured technical delivery support

Contract

3 to 15 years

Negotiable

16 October 2025

PBT Group is seeking a highly skilled Machine Learning Engineer to design, build, and deploy scalable machine learning solutions across complex data environments. The successful candidate will work closely with data scientists, data engineers, and business stakeholders to operationalise machine learning models, optimise data pipelines, and contribute to the continuous improvement of advanced analytics solutions.

This role requires a blend of strong data engineering expertise, applied machine learning knowledge, and cloud-based solution experience.

Key Responsibilities

• Design, develop, and deploy machine learning models into production environments.
• Build and maintain end-to-end ML pipelines for data ingestion, transformation, feature engineering, model training, and inference.
• Collaborate with data scientists to move models from experimentation to production.
• Optimise model performance and ensure scalability, reliability, and monitoring of ML systems.
• Implement MLOps best practices, including CI/CD automation, version control, model tracking, and reproducibility.
• Work with data engineers to ensure robust data quality, governance, and accessibility.
• Research and experiment with emerging AI/ML techniques and tools to enhance capabilities.
• Document processes and provide technical guidance to cross-functional teams.

Technical Skills & Experience

• Programming: Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
• ML Lifecycle Management: Experience with MLflow, Kubeflow, SageMaker, or similar platforms.
• Data Pipelines: Solid understanding of ETL/ELT processes and tools such as Airflow, Spark, or Databricks.
• Cloud Platforms: Hands-on experience with AWS, Azure, or GCP (data and AI services).
• Databases: Strong SQL skills and experience with both relational and NoSQL data stores.
• Model Deployment: Experience deploying ML models via APIs, containers (Docker, Kubernetes), or cloud endpoints.
• Version Control & CI/CD: Git, Jenkins, or GitHub Actions.
• Bonus: Exposure to Deep Learning, NLP, or Computer Vision frameworks.

Soft Skills

• Strong problem-solving and analytical skills.
• Excellent communication and collaboration with both technical and business stakeholders.
• Proactive and curious mindset, with the ability to learn and adapt quickly.
• Strong documentation and presentation abilities.

Minimum Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field.
• 3+ years of experience in applied machine learning or AI solution development.

• Proven track record of delivering production-ready ML models in real-world environments.

• In order to comply with the POPI Act, for future career opportunities, we require your permission to maintain your personal details on our database. By completing and returning this form you give PBT your consent

• If you have not received any feedback after 2 weeks, please consider you application as unsuccessful.

Data ScienceMachine LearningSQLPythonExtract Transform Load (ETL)Spark MLArtificial Intelligence

BankingFinanceInsurance

Purpose of the Role

The Machine Learning Engineer is responsible for
deploying, monitoring, and maintaining ML models in production
. They turn prototype models into scalable, production-grade systems by building automated pipelines, integrating with infrastructure, and ensuring data and model quality. They work closely with Data Scientists, Data Engineers, and MLOps Support to ensure models are reliable, performant, and aligned with business objectives.

Key Responsibilities1. Model Deployment & Pipeline Automation

• Translate models from notebooks to reusable, production-grade code.
• Build CI/CD pipelines for ML (unit tests, integration tests, automated deployment).
• Manage versioning of code, data, and models (e.g., Git, DVC).

2. Monitoring, Scaling & Performance

• Monitor live models for drift, latency, and failure.
• Tune models and pipelines for performance and cost-efficiency.
• Implement load testing and alerting (Prometheus, Grafana, Azure Monitor).

3. Data Integration & Governance

• Collaborate with Data Engineers to manage feature pipelines and real-time data flow.
• Ensure training/inference data meets governance and compliance requirements.
• Implement Feature Store solutions where relevant (e.g., Azure Feature Store).

4. Documentation & Support Enablement

• Provide clear documentation for handover to MLOps support.
• Define IAM roles and controls for model access across dev/test/prod.
• Lead training or walkthroughs for deployment best practices.

5. Continuous Improvement

• Automate repetitive tasks (testing, retraining, rollback triggers).
• Introduce modern techniques like streaming inference, canary deployments, or serverless ML.
• Participate in post-mortems and incident reviews to strengthen MLOps maturity.

Required Skills & Experience
Education

• Bachelor's degree in Computer Science, Data Science, Engineering, or similar.
• Master's degree preferred.

Experience

Intermediate
2–3 yrs Deploy models, build basic CI/CD, script pipelines

Senior
4+ yrs Scale production ML, lead infra design, mentor others

Technical Skills

• Languages
  : Python (required), PySpark, SQL.
• Cloud
  : Azure ML stack (Azure ML, DevOps, Feature Store).
• CI/CD
  : Git, Jenkins, Azure Pipelines.
• Monitoring
  : Azure Monitor, Prometheus, Grafana.
• Data Tools
  : Spark, Kafka (bonus).
• Security
  : IAM, data governance, model audit logging.

Competencies

Competency Expectation
Problem Solving
Debug and optimise model pipelines; fix deployment failures
Innovation
Automate, optimise, and introduce emerging MLOps practices
Communication
Explain infra to both technical and non-technical stakeholders
Teamwork
Collaborate across DS, DE, and Support; mentor juniors
Change Advocacy
Champion new tools, frameworks, or practices in ML lifecycle

Performance Metrics

• Model deployment success rate, rollback frequency, MTTD/MTTR.
• Model latency, throughput, and drift over time.
• Business value metrics linked to model performance (e.g., cost savings, conversion).

Develop domain-specific ML models

Leading end-to-end lifecycle of ML projects from data preparation and model training to deployment and monitoring

Optimizing existing ML models for scalability and performance in production environments

Document workflows

Matric / Grade 12

Tertiary Qualification in Information Communication Technology (ICT)

Relevant Cloud Certification

Min 3 - 6 Years experience as a Machine Learning Engineer

Between 3 - 5 Years