AI/ML Architect - Banking

Unison Group; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Unison Group seeks an AI/ML Architect with 10+ years in data and analytics, including 6+ in ML/AI and 2+ on GenAI production. You will lead cross-disciplinary teams to deploy governance-driven AI solutions in banking, ensuring explainability, monitoring, and compliance.

The role involves integrating LLM platforms, ensuring entitlements, and coordinating with risk and audit stakeholders to deliver scalable AI systems.

Qualifications

• 10+ years in data and analytics, incl 6+ in ML/AI, 3+ in banking and 2+ on production GenAI
• Must-have skills cover banking AI governance and model risk reviews
• Good-to-have: enterprise LLM platforms and data governance technologies

Responsibilities

• Leading multidisciplinary teams across data engineering, analytics, AI/ML and business domains
• Presenting AI designs and controls to model risk, audit and business stakeholders
• Drive production-ready ML/GenAI solutions within banking environments

Skills

Python

Spark

TensorFlow

PyTorch

scikit-learn

SQL

Tools

Databricks Mosaic AI

Genie

Unity Catalog

sqlglot

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.