Machine Learning Scientist

SUMMARY:
If you get excited about pushing the boundaries of Generative AI, building RAG frameworks, and turning raw data into powerful business solutions, then this is the playground you’ve been looking for.

POSITION INFO:
Join a cutting-edge data and AI team that’s shaping the future of how businesses use machine learning and generative AI. This hybrid role (3 days in office) will see you working on high-impact projects where your models go into production — not just notebooks.

You’ll work with LLMs, RAG frameworks, and scalable data pipelines, collaborate with DevOps and product teams, and be part of a culture that values experimentation, innovation, and continuous learning.

What’s In It For You?:

• 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: Bachelor’s Degree in Computer Science, Data Science, Machine Learning, or a related field (Master’s/PhD preferred).
• Experience: 3+ years in AI/ML, with at least 1–2 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.

• Actuarial roles (Life, Short-Term, Health, Pensions, and Quantitative)
• Data Scientists / Data Analysts (Python, R, SQL, and Machine Learning)
• Risk Analysts (Credit, Market, Model Risk, and Operational)
• Pricing Specialists (Insurance and Financial Products)
• Machine Learning & AI Data Scientists (ML Ops, NLP, and Predictive Modelling)
• Quantitative Specialists across Banking, Insurance, and FinTech