AI/ML Architect - Banking
Unison Group Open · verified Oct 22, 2026 At a glance
Mid-level Machine Learning Engineering role at Unison Group. Singapore · contract.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Take AI and machine-learning systems through bank model-risk or AI-governance reviews and into production
- Design RAG, text-to-SQL and agentic analytics for insight generation, metadata discovery and query creation
- Connect enterprise LLM platforms while enforcing access entitlements, row-level security and prompt-response audits
- Assess LLM output for accuracy, SQL validity, hallucinations and guardrail performance
- Lead cross-disciplinary delivery and present AI designs and controls to risk, audit and business stakeholders
What you bring
- At least ten years in data and analytics, including six in AI or machine learning and three in banking
- Hands-on knowledge of responsible AI controls, including explainability, audit trails, human review and model monitoring
- Experience with production GenAI architecture, code intelligence and generating traceable specifications, code and tests from metadata
- Strong ML engineering skills across Python, distributed data processing, model deployment and monitoring
Who this fits
This contract role suits a mid-career AI/ML architect who can lead across technical and business teams and explain controls to risk and audit stakeholders. The position is onsite in Singapore.
From the employer
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.