Data & AI Architect

**Responsibilities:** * Define the target architecture for enterprise analytics, Generative BI, machine learning and AI\-enabled delivery capabilities. * Design secure integration between governed data platforms, semantic layers, enterprise LLMs, vector stores, agents and BI tools. * Establish reusable patterns for RAG, feature engineering, model development, deployment, evaluation and monitoring. * Define AI security, privacy, entitlement, human\-review, explainability, audit and responsible\-AI controls. * Design AI\-enabled requirements discovery, impact analysis, mapping, code generation and test\-automation frameworks. * Set architecture standards for MLOps, DataOps, model registries, prompt/version management, observability and rollback. * Review AI/ML designs, data suitability, evaluation criteria, NFRs and production\-readiness evidence. * Guide data scientists, ML engineers, data engineers and platform teams and resolve cross\-platform technical risks. **FUNCTIONAL / DOMAIN SKILLS** * AI and analytics use cases in regulated banking, including AML, risk, payments, finance and customer analytics. * Model governance, validation, responsible AI, privacy, audit readiness and human\-in\-the\-loop controls. * Generative BI, semantic analytics and governed enterprise AI adoption. **TECHNICAL SKILLS** * Enterprise LLM platforms, RAG, vector databases, agent frameworks, Python, Spark, TensorFlow/PyTorch and MLflow. * Hadoop/Cloudera, Lakehouse platforms, semantic layers, APIs, containers, Kubernetes and cloud AI services. * MLOps, evaluation, monitoring, drift detection, CI/CD, observability and secure AI integration.