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.

Requirements:

Must have overall 15+ years of experience Information Technology with minimum of 5+ years of experience in Banking industry.

FUNCTIONAL / DOMAIN SKILLS

. AI and analytics use cases in regulated banking, including 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, 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.