Machine Learning Engineer
Data Centrix Johannesburg
QUALIFICATIONS and EXPERIENCE
AI Solution Design & Development
- Relevant Degree or Diploma in Computer Science, Data Science, Artificial Intelligence, Information Systems, Engineering, or a related field.
- Certifications such as Microsoft Certified: Azure AI Engineer Associate, Azure Data Scientist Associate, or equivalent (preferred but not mandatory).
- Minimum 3+ years of experience in AI/ML engineering, data science, or building AI-enabled solutions in a production environment.
- Hands-on experience developing and deploying LLM and/or generative AI solutions (e.g. RAG, chatbots, AI agents, intelligent document processing).
- Experience integrating AI services into existing business applications, ideally within the Microsoft ecosystem (Azure, Power Platform, Microsoft 365).
- Proven track record of delivering AI solutions that achieved measurable business outcomes.
- Experience working in cross-functional teams alongside business stakeholders, developers, and data engineers.
- Strong proficiency in Python and common AI/ML libraries (e.g. scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel).
- Hands-on experience with Azure AI Services, Azure OpenAI, Azure Machine Learning, and Azure AI Foundry.
- Experience building LLM-based solutions: prompt engineering, RAG, embeddings, vector databases, and AI agents.
- Working knowledge of Microsoft Power Platform (Power Apps, Power Automate, Power BI, Dataverse) and AI Builder / Copilot Studio.
- Solid understanding of data engineering: SQL, APIs, data pipelines, and working with structured and unstructured data.
- Familiarity with MLOps, version control (Git), CI/CD, and cloud deployment patterns.
- Strong analytical, problem-solving, and solution-design abilities.
- Excellent interpersonal and communication skills; able to translate business needs into technical solutions and vice versa.
- Awareness of Responsible AI principles, data privacy, and AI security best practices.
AI Solution Design & Development
- Identify, evaluate, and prioritise AI/ML use cases across the business and translate them into deployable solutions.
- Design, develop, and deploy AI models and intelligent applications using Python, Azure AI Services, Azure OpenAI, and related frameworks.
- Build and fine-tune Large Language Model (LLM) solutions, including Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, and AI agents.
- Develop predictive models, classification systems, computer vision pipelines, and intelligent document processing solutions where applicable.
- Manage the full AI solution lifecycle: data exploration, model development, testing, deployment, monitoring, and continuous improvement.
- Embed AI capabilities into existing Microsoft Power Platform solutions (Power Apps, Power Automate, Power BI, Power Pages, Dataverse) using AI Builder, Azure AI Foundry, and custom connectors.
- Integrate AI services into current line-of-business systems through APIs, custom connectors, Azure Functions, and Logic Apps.
- Enhance Power BI reporting with AI-driven insights, forecasting, anomaly detection, and natural-language Q&A.
- Build Copilot Studio agents and conversational interfaces that interact with internal systems and data sources.
- Work with structured and unstructured data sources to prepare, clean, and engineer features for AI models.
- Implement MLOps practices including version control, CI/CD for models, monitoring, drift detection, and responsible retraining.
- Ensure AI solutions are secure, scalable, observable, and aligned with cloud architecture best practices on Microsoft Azure.
- Partner with departments to understand workflows and identify where AI can deliver measurable impact.
- Collaborate with the Digital Transformation team to align AI initiatives with the broader digital roadmap.
- Provide user training, documentation, and ongoing support to drive adoption of AI-enabled tools.
- Communicate complex AI concepts in clear, business-friendly language to non-technical stakeholders.
- Apply Responsible AI principles: fairness, transparency, accountability, privacy, and security.
- Establish and enforce governance frameworks for AI usage, including data handling, model approval, and acceptable-use policies.
- Monitor model performance, bias, and hallucination risks; implement guardrails for generative AI solutions.
- Ensure compliance with POPIA, Company Group standards, and applicable data protection and security requirements.
- Stay current with advances in AI, generative models, agentic systems, and the Microsoft AI stack.
- Lead AI proofs-of-concept and pilots, then scale successful initiatives enterprise-wide.
- Review existing systems and recommend AI-driven enhancements that improve performance, accuracy, or user experience.
- Manage multiple AI initiatives concurrently from conception through deployment and handover.
- Produce technical and functional documentation including solution architecture, data flows, model cards, SOPs, and support guides.
- Ensure adherence to company standards, security requirements, and statutory regulations.
Network RecruitmentJohannesburg
Key Responsibilities
• Design, build, and deploy AI/ML and multimodal models in production environments
• Develop and integrate AI-driven features using Python
• Design and optimise MongoDB schemas for performance and scalability
• Build and...
Network RecruitmentJohannesburg
Key Duties & Responsibilities
• Design, build, and deploy AI/ML models, including multimodal systems
• Develop and integrate AI functionality into production systems using Python
• Design and optimise MongoDB schemas for scalability and...
Network RecruitmentJohannesburg
This is a handsâon technical role for an experienced machine learning professional who enjoys working endâtoâend on complex models in a regulated environment and providing strong analytical challenge to production models.
You will play a key...