Data Architect

Role & responsibilities

• Lead the end-to-end design and implementation of data science solutions for business-critical applications, ensuring scalability, performance, and compliance.
• Conduct advanced data profiling, cleansing, and exploratory data analysis to uncover actionable insights and inform model development.
• Develop, validate, and deploy AI/ML models (including supervised, unsupervised, and deep learning algorithms) using Python, R, and modern ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Design and implement Generative AI solutions (e.g., LLMs, RAG, AI agents) for enterprise use cases, leveraging platforms such as OpenAI, Azure AI, or AWS SageMaker.
• Champion data governance, responsible AI principles, and model interpretability, ensuring solutions are ethical, transparent, and compliant with regulatory standards.
• Collaborate with cross-functional teams (business, engineering, product, and leadership) to gather requirements and translate business needs into technical solutions and strategic data science roadmaps.
• Provide technical leadership and mentorship across data science projects, conducting design reviews and enforcing development standards.
• Architect and optimize data pipelines for model training, evaluation, and deployment, integrating with core business systems and cloud platforms.
• Develop and maintain documentation for data science workflows, model architectures, and integration patterns.
• Stay current with emerging trends in AI/ML, GenAI, and data engineering, acting as a subject matter expert and driving continuous innovation.

Preferred candidate profile

• Proven, hands-on experience designing and deploying solutions using:
•   - Data profiling and analysis tools (e.g., Pandas, SQL, Power BI)
•   - Machine learning frameworks (scikit-learn, TensorFlow, PyTorch)
•   - Generative AI platforms (OpenAI, Azure AI, AWS SageMaker)
•   - Python and R for data science and ML
• Strong understanding of machine learning algorithms, model lifecycle management, and MLOps best practices.
• Demonstrable experience in designing data governance, data strategy, and responsible AI frameworks.
• Deep knowledge of cloud-native architecture and data engineering (Azure, AWS, or GCP).
• Experience with data pipelines, feature engineering, and model deployment in production environments.