For those who dont just write code but launch innovations. This isnt your average data science role. Join a tight-knit team building ML/AI features that power real-time insights across massive, distributed systems. Youll be hands-on from idea to deployment, navigating complexity with a clear sense of purpose. Whether you’re scripting in Unix, scaling microservices, or wrangling data for ML models, your work will land where it mattersin systems relied on worldwide. Expect freedom to explore, ownership of outcomes, and a collaborative culture that backs both excellence and experimentation. Its not just about building thingsit’s about launching things that work at scale and make a difference. Skills & Experience: Minimum 4 years in data science or data-focused engineering Production-grade experience deploying ML/AI models Strong in Python and/or Java Solid with SQL, large datasets, and database design Data wrangling, feature engineering, and model evaluation Proficient in Unix scripting and troubleshooting Comfortable with CI/CD for ML workflows, microservices, and containers Bonus: audio processing, NLP, LLM, or RAG techniques Kafka, gRPC, Docker, Vertica, Grafana, Elasticsearch experience beneficial Qualification: Bachelors in Data Science, Computer Science, Engineering, or related Masters preferred Contact DYLAN MAWONA on

What youll be building: ML/AI features that run inside high-volume, real-time distributed systems Microservices that scale dynamically and integrate seamlessly Data pipelines that transform chaos into clarity Solutions that go from whiteboard to client deploymentwith zero fluff Your toolbox includes: Python, Java, SQL Unix-based environments, CI/CD pipelines Kafka, containers, microservices Feature engineering, model evaluation, version control Production-grade ML workflows Youll thrive here if: Youve deployed models into production and debugged them under pressure Think in algorithms but communicate like a product owner Youve mentored juniors and shaped technical direction Youve worked with NLP, audio processing, LLMs or RAGand you know what works in the wild You understand distributed systems and how to make them behave Bonus points if youve touched: Real-time event systems Telecom, signal processing or IP networks Scalable ML services that dont just runthey evolve This is not a sandbox. Its a launchpad for engineers who want their code to matter