ML & Data Science Architect

We are looking for an experienced ML & Data Science Architect to lead the design and evolution of Bosenet’s machine learning and data science ecosystem. This role is focused on platform architecture, model governance, MLOps maturity, and scalable ML infrastructure. You will define the standards, tooling, and operational frameworks that enable ML Engineers and Data Scientists to build production-ready machine learning systems efficiently and responsibly. You will work closely with AI Architects, Engineering Leaders, Product Teams, and Business Stakeholders to ensure ML investments align with measurable business outcomes.

ML Platform Architecture • Design and own scalable ML platform architecture. • Define standards for feature stores, model registries, experiment tracking, and serving infrastructure. • Build frameworks for reproducible and production-ready ML systems. MLOps & Automation • Establish enterprise-grade MLOps processes. • Design automated retraining pipelines, monitoring systems, rollback mechanisms, and deployment workflows. • Implement drift detection, model monitoring, and ML observability. Data Architecture & Governance • Design scalable data platforms including lakehouses, feature engineering pipelines, and lineage tracking. • Define data governance, quality, and compliance standards. • Ensure reproducibility and auditability across ML workflows. Model Governance & Responsible AI • Define standards for model explainability, fairness, bias detection, and evaluation. • Create governance policies for production model approvals. • Standardize ML documentation and model lifecycle management. Leadership & Collaboration • Lead architecture reviews for ML systems and data platforms. • Mentor ML Engineers and Data Scientists. • Partner with engineering and business teams to align ML strategy with organisational goals. • Present ML performance and platform strategy to executive stakeholders.

& Experience • 5–8+ years of experience in Machine Learning or Data Science with at least 2 years in an Architect, Platform Lead, or Principal Engineer role. • Strong experience designing enterprise ML platforms and MLOps ecosystems. • Hands-on expertise with: • MLflow • Kubeflow • ZenML • Airflow • Feature Stores • Experiment Tracking • Strong knowledge of ML frameworks: • PyTorch • TensorFlow • Scikit-learn • Experience with data platforms and modern data architecture: • Delta Lake • Apache Iceberg • ELT Pipelines • Data Warehouses • Hands-on experience with cloud ML services: • AWS SageMaker • Azure ML • GCP Vertex AI • Strong understanding of statistical modelling, evaluation metrics, and responsible AI practices. • Proficiency in Python, SQL, Spark, and data engineering tooling. • Strong communication and stakeholder management capabilities. Nice-to-Have Skills • Cloud ML or Data Engineering Certifications. • Experience with real-time ML inference systems. • Experience with GenAI infrastructure and embedding pipelines. • Exposure to financial forecasting, OCR systems, NLP, or document intelligence. • Background in research-driven or highly regulated environments. Core Technology Stack • Python / SQL / PySpark • PyTorch / TensorFlow • MLflow / Weights & Biases / DVC • Kubeflow / Airflow / ZenML • Feast / Tecton • Delta Lake / Apache Iceberg • SageMaker / Vertex AI / Azure ML • Docker / Kubernetes • dbt / ELT Pipelines • SHAP / LIME • CI/CD for ML • Real-Time Inference Systems • Responsible AI Frameworks