AI/ML Architect - Banking

Unison Group; Singapore; On-site; SGD 180,000 - 300,000; Full time

Job summary

Unison Group in Singapore seeks an AI/ML Architect with 10+ years in data and analytics, including 6+ in ML/AI, 3+ in banking and 2+ on production GenAI.

You will lead multidisciplinary teams across data engineering, analytics, AI/ML and business domains, and present AI designs and controls to model risk, audit committees and business stakeholders.

Qualifications

• 10+ years in data and analytics.
• 6+ years in ML/AI roles.
• 3+ years in banking environments.
• 2+ years deploying GenAI to production.

Responsibilities

• Lead multidisciplinary teams across data engineering, analytics, AI/ML and business domains.
• Present AI designs and controls to model risk, audit and business stakeholders.
• Ensure governance reviews and compliance with AI governance principles.

Skills

AI governance

Model risk

Responsible AI

Banking analytics

GenAI production

Tools

LangGraph

Azure OpenAI

AWS Bedrock

Vertex AI

sqlglot

Databricks Mosaic AI

Genie

Unity Catalog

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.