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.