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UN
Unison Group

Gen AI Engineer - Banking

LocationSingapore, Singapore, Singapore
Typecontract
SenioritySenior
Experience8+ yrs
DepartmentManish
Company size1,001–5,000 people
First seenOct 5, 2026 · 6d ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Must have11
8+ years in software or data engineering, including 2+ on LLM applications and 3+ in banking or regulated financial services
Building software in regulated environments: authentication, entitlements, audit logging and data privacy
Applying row-level access and human review steps to AI outputs
LLM applications beyond proof of concept: RAG, embeddings, vector stores and structured outputs
Text-to-SQL over a semantic layer; conversational retrieval of reports and KPIs
Agent frameworks such as LangGraph or LlamaIndex
Evaluation: accuracy, SQL correctness, hallucination testing and guardrails, automated in CI/CD
Code parsing and lineage extraction for SQL, PL/SQL, SAS and Python (e.g. sqlglot, ANTLR)
Strong Python and SQL
Working with enterprise data at warehouse scale
Git, CI/CD and MLflow
Nice to have6
Enterprise LLM platforms: Azure OpenAI, AWS Bedrock or Vertex AI
Databricks Mosaic AI or Genie
Visualization libraries (Plotly, ECharts, Vega) and Qlik or Power BI APIs
Databricks Generative AI Engineer Associate
Azure AI Engineer
AWS ML Specialty
Skills
Python
SQL
LLM
RAG
embeddings
vector stores
LangGraph
LlamaIndex
Git
CI/CD
MLflow
sqlglot
ANTLR
Azure OpenAI
AWS Bedrock
Vertex AI
Databricks Mosaic AI
Databricks Genie
Plotly
ECharts
Vega
Qlik
Power BI
We are looking for Gen AI Engineer with 8+ years in software or data engineering, including 2+ on LLM applications and 3+ in banking or regulated financial services.
Must-have skills
Banking and controls
• Building software in regulated environments: authentication, entitlements, audit logging and data privacy.
• Applying row-level access and human review steps to AI outputs.
Generative AI engineering
• LLM applications beyond proof of concept: RAG, embeddings, vector stores and structured outputs.
• Text-to-SQL over a semantic layer; conversational retrieval of reports and KPIs.
• Agent frameworks such as LangGraph or LlamaIndex.
• Evaluation: accuracy, SQL correctness, hallucination testing and guardrails, automated in CI/CD.
• Code parsing and lineage extraction for SQL, PL/SQL, SAS and Python (e.g. sqlglot, ANTLR).
Software and data engineering
• Strong Python and SQL.
• Working with enterprise data at warehouse scale.
• Git, CI/CD and MLflow.
Good-to-have skills
• Enterprise LLM platforms: Azure OpenAI, AWS Bedrock or Vertex AI.
• Databricks Mosaic AI or Genie.
• Visualization libraries (Plotly, ECharts, Vega) and Qlik or Power BI APIs.
Certifications (preferred): Databricks Generative AI Engineer Associate; Azure AI Engineer; AWS ML Specialty.
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