• przestrzeganie dobrych praktyk tworzenia oprogramowania oraz dbanie o ich aktualność, we współpracy z zespołem seniorów ds. uczenia maszynowego, • wspieranie lidera projektowego oraz przełożonego w inicjatywach związanych z Machine Learning, • dążenie do przestrzegania firmowych standardów i kierunków rozwoju w obszarze uczenia maszynowego, • zaangażowanie w procesy analityczne i doradcze, związane z wdrażaniem rozwiązań opartych na AI, • zaangażowanie w proces presale, • monitorowanie trendów rynkowych oraz dostosowywanie rozwiązań względem wymagań biznesowych, • udzielanie wsparcia merytorycznego w ramach strategicznych działań wewnątrz firmy.

• Bridge research and production, building AI models and agent systems that ship into real products. • Architect, evaluate, and optimize autonomous voice and digital agents powering 10M+ customer interactions per month across West Africa. • Build for the hardest edge cases: poor connectivity, low literacy, and languages with little training data. • Experiment with, evaluate, and integrate the latest voice and text models. • Own problems end-to-end, from problem discovery to running in production, working alongside product and engineering leaders who prioritize shipping real customer impact.

• Design and construct Ikue's AI Studio in collaboration with Product owners and Data Scientists • Design and build machine learning pipelines (model build, evaluation, deploy, monitoring) • Integrate machine learning outputs into real-time and batch data pipelines • Ensure machine learning and data pipelines are monitored, reliable and supportable (including expert support when required)

• **Algorithm Development:** Design and train models to solve specific physical problems (e.g., machine uptime detection or production count prediction). • **Signal Processing:** Apply statistical methods to raw time-series data to extract meaningful features and reduce noise. • **Validation:** Define and monitor metrics (accuracy, recall, precision) to validate model performance on real-world data before and after deployment. • **Model Serving:** Develop and maintain RESTful APIs (using frameworks like FastAPI) to expose your models for real-time inference. • **Production Standards:** Write clean, modular, and testable Python code. You are expected to use version control, write unit tests, and follow software design patterns. • **Performance Optimization:** Profile and optimize model inference code to ensure low latency and efficient resource usage.