Johannesburg: AI Engineer

This role is with a leading technology company focusing on enterprise network and digital solutions. Youll join a newly established AI transformation initiative, contributing to the development of AI infrastructure, applications, and operational workflows. The position offers a chance to shape AI strategy, deploy real-world ML/DL models, and collaborate with product managers, engineers, and data scientists. You will gain hands-on experience with modern AI frameworks, cloud platforms, MLOps practices, and cutting-edge data engineering pipelines, all within a collaborative and technically rigorous environment. The company promotes continuous learning, innovation, and career growth in AI and machine learning domains. Key Responsibilities: Design, train, and deploy ML/DL models (NLP, computer vision, time series, generative AI) and build AI applications with LLMs. Develop, maintain, and optimise data pipelines; gather, clean, and engineer features from diverse data sources. Deploy AI models to production using containerization (Docker) and orchestration (Kubernetes), ensuring operational reliability. Collaborate with business and technical stakeholders to translate problems into AI solutions. Monitor, evaluate, and maintain AI systems, including CI/CD pipelines, model versioning, and lifecycle management. Contribute to AI platform development, infrastructure-as-code, and workflow orchestration for complex pipelines. Continuously research emerging AI/ML technologies, experiment with new techniques, and share knowledge with the team. Job Experience and Skills Required: Education: Bachelors or Honours degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Information Systems, or related quantitative field. Graduates within the last 18 months or completing studies in 2025 are encouraged to apply. Experience: Early-career candidates with demonstrable experience through projects, internships, competitions (Kaggle/hackathons), or portfolio work. Skills: Programming: Python (preferred), or similar languages (Java, JavaScript, Go). Database knowledge: SQL and NoSQL, vector databases exposure advantageous. ML fundamentals: basic understanding of machine learning concepts, algorithms, and statistics. Cloud platforms: AWS, Azure, or GCP. Tools: Git, containerization (Docker), workflow orchestration tools (Airflow, Prefect, Dagster) beneficial. Communication: ability to present technical concepts to business and technical audiences. Advantageous: Exposure to ML frameworks (TensorFlow, PyTorch, scikit-learn), LLMs, generative AI, MLOps platforms (MLflow, Weights & Biases). Familiarity with prompt engineering, feature engineering, and responsible AI practices. Experience with REST APIs, CI/CD pipelines, and infrastructure-as-code tools. Apply now! For more exciting IT & AI vacancies