Principal Consultant

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.

Key Domain/ Technical Skills:

FUNCTIONAL / DOMAIN

• 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

• Enterprise LLM platforms, RAG, vector databases, agent frameworks, Python, Spark, Tensor Flow/PyTorch and MLflow.
• Hadoop/Cloudera, Lakehouse platforms, semantic layers, APIs, containers, Kubernetes and cloud AI services.
• ML Ops, evaluation, monitoring, drift detection, CI/CD, observability and secure AI integration.