AI/ML Architect - Banking

Job description

We are looking for AI/ML Architect with 10+ years in data and analytics, including 6+ in ML/AI, 3+ in banking and 2+ on production GenAI

Must-have skills

Banking and AI governance • Taking AI/ML solutions to production inside banks through model risk or AI governance review. • Responsible AI: explainability, audit trails, human-in-the-loop controls and model monitoring (e.g. MAS FEAT principles). • Financial crime data: AML models, alerts and case data.

Generative AI architecture • RAG, text-to-SQL over semantic layers, and agentic analytics for insight generation, metadata discovery and query generation. • Integration with enterprise LLM platforms, enforcing entitlements, row-level security and prompt/response audit. • LLM evaluation: accuracy, SQL correctness, hallucination testing and guardrails. • Code intelligence: parsing SQL, PL/SQL, SAS and Python to extract mappings, lineage, dependencies and business rules. • Generating specifications, code and tests from metadata, with traceability.

ML Engineering • Python, Spark, Scala or Java; TensorFlow, PyTorch and scikit-learn. • MLflow, feature engineering, model deployment and monitoring; DataOps practices. • Large-scale data on Hadoop and Lakehouse platforms; semantic layers, knowledge graphs and federated governance.

Delivery and communication • Leading multidisciplinary teams across data engineering, analytics, AI/ML and business domains. • resenting AI designs and controls to model risk, audit and business stakeholders.

Good-to-have skills • Agent frameworks such as LangGraph; vector stores. • Enterprise LLM platforms: Azure OpenAI, AWS Bedrock, Vertex AI or on-premises models. • Databricks Mosaic AI, Genie and Unity Catalog; SQL parsers such as sqlglot.

Certifications (preferred): Databricks Generative AI Engineer or ML Professional; Azure AI Engineer; AWS ML Specialty; Google Professional ML Engineer.