Data Scientist

Responsibilities:

• Analyze production logs to identify recurring failure patterns.
• Design and implement custom evaluations that go beyond standard metrics.
• Fine-tune and align models (LoRA, full fine-tuning, DPO) for domain-specific tasks.
• Build and maintain RAG pipelines with a focus on precision and recall.
• Create guardrail systems (classifiers, rule layers, confidence scoring) to filter or correct model outputs.
• Document failure modes and share findings across engineering and product teams.
• Partner with engineers to deploy models into production environments.
• Own the full lifecycle: data collection, model training, evaluation, deployment, monitoring.

Requirements:

• Minimum of 4+ years’ experience in data science or machine learning engineering.
• 2+ years’ experience working with LLMs in production environments.
• Experience in Python Programming and a proven track record of building and deploying custom AI models (not just API wrappers).
• You must have created projects and solutions for LLMs to solve and they failed to solve and they failed to solve the problem accurately.
• Deep understanding of transformers, attention, and model architectures.
• Experience with PyTorch, HuggingFace Transformers, and LangChain or similar frameworks.
• Strong Knowledge of retrieval systems (vector databases, hybrid search, reranking).
• Familiarity with MLOps tools (MLflow, Weights and Biases, Kubernetes).
• Solid software engineering practices: testing, version control, code review.

Remuneration: NGN 600,000