Unison Group

Builds financial risk and compliance systems

AI/ML Architect - Banking

Applied Machine Learning +1Confirmed live in the last 24 hours

Full-timeSingaporeNo salary listedIn-person

About the Job

Requirements

At least 10 years of experience in data and analytics, including at least 6 years in machine learning or artificial intelligence, at least 3 years in banking, and at least 2 years working on production generative artificial intelligence.
Experience taking AI/ML solutions into production in banks through model risk or AI governance review.
Knowledge of responsible AI practices, including explainability, audit trails, human-in-the-loop controls, and model monitoring, such as MAS FEAT principles.
Experience with financial crime data, including anti-money laundering models, alerts, and case data.
Knowledge of retrieval-augmented generation, text-to-SQL over semantic layers, and agentic analytics for insight generation, metadata discovery, and query generation.
Experience integrating with enterprise large language model platforms while enforcing entitlements, row-level security, and prompt/response auditing.
Experience evaluating large language models for accuracy, SQL correctness, hallucinations, and guardrails.
Ability to parse SQL, PL/SQL, SAS, and Python to extract mappings, lineage, dependencies, and business rules.
Experience generating specifications, code, and tests from metadata with traceability.
Proficiency in Python and at least one of Spark, Scala, or Java.
Experience with TensorFlow, PyTorch, and scikit-learn.
Experience with MLflow, feature engineering, model deployment and monitoring, and DataOps practices.
Experience with large-scale data on Hadoop and Lakehouse platforms, semantic layers, knowledge graphs, and federated governance.

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.

Desired Qualifications

Experience with agent frameworks such as LangGraph and vector stores.
Experience with enterprise LLM platforms such as Azure OpenAI, AWS Bedrock, Vertex AI, or on-premises models.
Experience with Databricks Mosaic AI, Genie, and Unity Catalog, and SQL parsers such as sqlglot.
Preferred certifications include Databricks Generative AI Engineer or ML Professional, Azure AI Engineer, AWS ML Specialty, and Google Professional ML Engineer.

AI/ML Architect - Banking

Unison Group · Singapore, Singapore · Contract · Posted 2026-09-25

Workplace: on_site

Department: Manish

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.

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About the Company

Unison Group

Builds financial risk and compliance systems

Unison Group builds technology systems for financial institutions that need to manage risk, meet reporting rules and modernize their operations. Its consultants work across liquidity, market and credit risk, combining financial knowledge with software implementation and data analysis. The business also designs cloud infrastructure and develops custom applications, connecting these systems with the information and processes clients already use. It earns money through consulting, technology implementation and engineering services for enterprises, with work ranging from system design to deployment. Founded as Unison Consulting in 2012, the group is headquartered in Singapore.

Company size: 201-500
Company stage: Not listed
Total funding: Not listed
Headquarters: Not listed
Founded: 2012

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