About this role
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
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Taking AI/ML solutions to production inside banks through model risk or AI governance review.
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Responsible AI: explainability, audit trails, human-in-the-loop controls and model monitoring (e.g. MAS FEAT principles).
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Financial crime data: AML models, alerts and case data.
Generative AI architecture
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RAG, text-to-SQL over semantic layers, and agentic analytics for insight generation, metadata discovery and query generation.
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Integration with enterprise LLM platforms, enforcing entitlements, row-level security and prompt/response audit.
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LLM evaluation: accuracy, SQL correctness, hallucination testing and guardrails.
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Code intelligence: parsing SQL, PL/SQL, SAS and Python to extract mappings, lineage, dependencies and business rules.
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Generating specifications, code and tests from metadata, with traceability.
ML Engineering
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Python, Spark, Scala or Java; TensorFlow, PyTorch and scikit-learn.
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MLflow, feature engineering, model deployment and monitoring; DataOps practices.
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Large-scale data on Hadoop and Lakehouse platforms; semantic layers, knowledge graphs and federated governance.
Delivery and communication
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Leading multidisciplinary teams across data engineering, analytics, AI/ML and business domains.
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resenting AI designs and controls to model risk, audit and business stakeholders.
Good-to-have skills
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Agent frameworks such as LangGraph; vector stores.
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Enterprise LLM platforms: Azure OpenAI, AWS Bedrock, Vertex AI or on-premises models.
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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.