AI/ML Architect - Banking
LocationSingapore, Singapore, Singapore
Typecontract
SeniorityLead
Experience10+ yrs
DepartmentManish
Company size1,001–5,000 people
PostedSep 25, 2026 · 2w ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
10+ years in data and analytics
6+ years in ML/AI
3+ years in banking
2+ years on production GenAI
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
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
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
Leading multidisciplinary teams across data engineering, analytics, AI/ML and business domains
Presenting AI designs and controls to model risk, audit and business stakeholders
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
Databricks Generative AI Engineer or ML Professional certification
Azure AI Engineer certification
AWS ML Specialty certification
Google Professional ML Engineer certification
Skills
Python
Spark
Scala
Java
TensorFlow
PyTorch
scikit-learn
MLflow
Hadoop
Databricks
LangGraph
Azure OpenAI
AWS Bedrock
Vertex AI
Databricks Mosaic AI
Databricks Genie
Databricks Unity Catalog
sqlglot
SQL
PL/SQL
SAS
RAG
LLM
LLM evaluation
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.