AI/ML Architect - Banking
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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.