Lead Data Scientist

Select One; Gauteng; On-site; ZAR 500 000 - 1 200 000; Full time

Job summary

A top-tier technology company in Gauteng is seeking a Machine Learning Engineer responsible for deploying and maintaining ML models in production. The ideal candidate will have a Bachelor's degree and expertise in Python and data tools. Strong experience in integrating ML solutions and ensuring data quality is essential. This role offers competitive remuneration ranging from R500K to R1.2M annually.

Qualifications

• 2-3 years of experience in deploying models and building CI/CD pipelines.
• Strong understanding of data governance and compliance requirements.

Responsibilities

• Deploy, monitor, and maintain ML models in production.
• Build CI/CD pipelines for ML.
• Collaborate with Data Engineers to ensure data quality and integration.

Skills

Python

PySpark

SQL

Education

Bachelor's degree in Computer Science, Data Science, or Engineering

Master's degree preferred

Tools

Spark

Kafka

Job description

Machine Learning Engineer

Centurion, Gauteng

Salary: R500000 - R1200000

Posted today

Job Description

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.

• 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.
• 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

• 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.
• Data Tools: Spark, Kafka (bonus).

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 latency, throughput, and drift over time.
• Business value metrics linked to model performance (e.g., cost savings, conversion).

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Machine Learning Data Scientist

Johannesburg, Gauteng

Network Recruitment

Posted 24 days ago

Job Description

This is your opportunity to work at the cutting edge of Generative AI, data engineering, and large-scale deployment, while collaborating with world-class teams to shape the future of intelligent systems.

• Develop & Enhance AI Models: Create, refine, and implement Generative AI systems and Retrieval-Augmented Generation (RAG) pipelines using frameworks like LangChain and Llama-Index.
• Data Engineering: Build scalable data workflows, manage ETL processes, and integrate both structured and unstructured data for AI-driven applications.
• Production Deployment: Deliver models into production, collaborating with DevOps to ensure performance across cloud and on-prem environments.
• Collaborate Across Teams: Work closely with Engineers, product owners, and business leaders to ensure AI solutions drive measurable value.
• Innovate & Research: Stay current with emerging architectures, frameworks, and methodologies, contributing to innovation and thought leadership.

Job Experience and Skills Required:

• Education: Degree in Computer Science, Data Science, Machine Learning, or a related field.
• Experience: 3+ years of professional experience in AI/ML, including at least 1-2 years in Generative AI.
• Practical experience with deep learning frameworks (TensorFlow and PyTorch) and generative AI libraries.
• Familiarity with cloud platforms (AWS, GCP, and Azure) and container technologies (Docker and Kubernetes).

• Skills: Strong programming ability in Python and SQL, plus experience with libraries like Pandas and NumPy.
• Solid understanding of machine learning algorithms, neural networks, and generative models.
• Knowledge of large-scale data storage (Hadoop, Spark, and Vector databases).
• Understanding of MLOps practices, including model lifecycle management, monitoring, and retraining.

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Machine Learning Engineer Lead

Posted today

Job Description

At LexisNexis we develop the legal profession's most innovative products for data analysis, visualization, and research. We use the latest techniques in AI, machine learning, and data visualization to uncover insights about legal matters, contracts and legal spend management.

We are looking for a skilled LLM Application Developer to join our team. You will be responsible for implementing large language model (LLM) based applications, working with proprietary and open-source models as well as popular frameworks such as LangChain or LlamaIndex to ensure seamless integration and deployment.

Responsibilities

• Manage a team of Machine Learning Engineers and Data Engineers
• Collaborate with stakeholders like Product Managers, Data Scientists and Program Managers
• Develop and implement LLM-based applications.
• Fine-tune and deploy large language models.
• Build RAG-based applications
• Integrate models with existing systems and APIs.
• Preprocess and manage data for training and deployment.
• Collaborate with cross-functional teams to define, design, and ship new features.
• Write clean, maintainable, and efficient code.
• Document development processes, code, and APIs.

Requirements

• Prior experience managing an engineering team
• Proven experience with large language models and open-source frameworks.
• Experience leveraging models from repositories such as Hugging Face
• Experience with deep learning frameworks such as PyTorch, Tensorflow and Hugging Face Transformers.
• Strong knowledge of API integration (RESTful, GraphQL).
• Experience with data preprocessing, SQL, and NoSQL databases as well as vector stores (e.g., Postgres, Elasticsearch/OpenSearch, ChromaDB etc.)
• Experience with GPU programming, including CUDA or RAPIDs
• Familiarity with deployment tools (Docker, Kubernetes).
• Excellent problem-solving and communication skills.
• Ability to work collaboratively in an agile team environment.

Preferred Qualifications

• Degree in Computer Science, Data Science, or related field.
• Certifications in machine learning, data science, or cloud computing.
• Portfolio showcasing past projects or contributions to open-source projects.

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Machine Learning Data Scientist

Johannesburg, Gauteng

The Legends Agency

Posted 21 days ago

Job Description

Machine Learning Engineer (12-Month Contract)

Join a leading AI consultancy delivering cutting-edge solutions for enterprise clients. Johannesburg | R600 - R640 per hour | Contract

About Our Client

Our client is a specialist consultancy that helps businesses harness the full potential of artificial intelligence. Partnering with large enterprises, including major players in financial services, they design and deliver impactful AI solutions that drive measurable results. The company is known for technical excellence, innovation, and a collaborative culture.

The Role: Machine Learning Engineer

We are seeking a Machine Learning Engineer to support a 12-month project within the banking sector. You will be responsible for building and scaling machine learning models, ensuring their seamless deployment into production environments. This is a hands-on role where you\'ll collaborate with data scientists, engineers, and business stakeholders to deliver end-to-end AI solutions.

Key Responsibilities

• Design, build, and optimise machine learning models for enterprise banking applications.
• Develop and implement scalable ML pipelines, integrating them into production systems.
• Collaborate with cross-functional teams to deliver robust AI solutions.
• Deploy, monitor, and maintain ML models within AWS environments.
• Ensure model reliability, reproducibility, and performance across their lifecycle.
• Document workflows, methodologies, and best practices for future use.

About You

3 - 5 years of experience in machine learning, data science, or related fields.

Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).

Experienced in working with large datasets and SQL/NoSQL databases.

Essential: Proven expertise with AWS cloud services (SageMaker, S3, Lambda, EC2, Glue, Redshift).

Knowledge of MLOps practices and CI/CD for ML pipelines.

Strong problem-solving skills with the ability to translate business needs into technical solutions.

Experience in banking or financial services is advantageous but not mandatory.

Bonus: Experience deploying AI models at scale, exposure to Docker/Kubernetes, and familiarity with ML observability tools.

Duration: 12 months (with potential for extension).

Location: Johannesburg or Stellenbosch, South Africa (hybrid flexibility may apply).

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Machine Learning Engineer Lead

Posted today

Job Description

About Our Client

Our client is a specialist AI consultancy, partnering with top enterprises to design and implement impactful artificial intelligence solutions. With a reputation for delivering scalable, practical, and high-value projects, they work at the cutting edge of data science and machine learning. Their collaborative, innovation-driven culture offers an environment where you can shape pioneering AI initiatives that influence entire industries.

The Role: Senior Machine Learning Engineer

This is a 6-month contract opportunity to lead the machine learning delivery of a greenfields proof-of-concept (POC) for one of South Africa's largest banks, reimagining the loyalty and rewards landscape. You will design and validate models, collaborate with technical and business teams, and ensure that solutions are scalable, robust, and strategically aligned.

Key Responsibilities

• 3 to 5 years of experience in machine learning engineering, applied AI, or data science
• Design, develop, and validate machine learning models for customer behaviour and rewards optimisation
• Collaborate with data engineers, solution architects, and client stakeholders to align technical solutions with business objectives
• Apply strong feature engineering, model evaluation, and reproducibility best practices
• Explore and test new modelling approaches to deliver measurable outcomes
• Ensure scalability and robustness with MLOps principles
• (Bonus) Contribute to operationalising AI solutions from POC to production

About You

3 to 5 years of proven experience building and deploying ML models in business contexts

Strong proficiency in Python, SQL, and ML libraries (scikit-learn, TensorFlow, PyTorch)

Solid understanding of model lifecycle management and cloud platforms (Azure preferred)

Excellent problem-solving, communication, and collaboration skills in a consulting environment

Experience in financial services, customer analytics, or loyalty/rewards is advantageous

Bonus: Hands-on experience in productionising AI solutions

Duration: 6 months (with potential extension)

Location: Johannesburg or Cape Town (hybrid / remote-first flexibility)

Start Date: Immediate availability preferred

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