Job Description - Solution Architect (Data & AI) - Gen AI, Modern Data
Contract Type:
Permanent
Location:
Singapore, Singapore
Date Published:
04-Mar-2026
Salary:
$300,000.00 - $300,000.00 Annual
Company introduction: A leading global technology services and delivery organisation is building out its Modern Data & AI capability in APAC. The team partners enterprise clients on large-scale digital transformation programmes - designing, building and showcasing scalable data platforms and AI solutions across cloud ecosystems. They are hiring a newly created permanent position of a Solution Architect (Data & AI) as a key part of their expansion plans.
Job responsibilities:
Run solution workshops, technical demos and POCs across modern data platforms, AI/ML and GenAI use cases.
Support RFP/RFI responses and proposal development, contributing to solution scope, feasibility and adoption strategy.
Architect and deliver solutions on modern data platforms such as Databricks, Snowflake, Microsoft Fabric, BigQuery, Cloudera CDP (lakehouse / distributed data systems).
Design and implement robust data engineering pipelines (e.g., Spark, Airflow, Delta Lake, Kafka, dbt) with production-grade reliability, observability and data quality.
Drive data governance and catalog implementations (e.g., Purview, Collibra, Unity Catalog, Alation, Informatica), including metadata, lineage, compliance and DQ policies.
Build and operationalise AI/ML and MLOps workflows (e.g., MLflow, Kubeflow, SageMaker, Azure ML) across training, deployment, registry and monitoring.
Deliver GenAI / RAG / agentic AI solutions using platforms such as Azure OpenAI, AWS Bedrock, Vertex AI, and frameworks like LangChain/LlamaIndex (plus vector DB integrations).
Provide architecture leadership on enterprise patterns such as Lakehouse, Data Mesh and Data Fabric, driving best practices and capability roadmaps.
Job requirements:
Proven hands-on experience delivering end-to-end Data & AI solutions in production (not just PoCs).
Engineering foundation strongly preferred (e.g., started in software engineering before moving into architecture), with proven hands-on depth - not just high-level diagrams.
Strong experience as a Solution Architect / Technical Architect delivering programmes end-to-end, with the ability to explain designs, trade-offs, and project outcomes clearly.
Strong modern data platform experience (Databricks/Snowflake/Fabric/BigQuery/Cloudera) and solid distributed systems fundamentals.
Strong data engineering toolkit: Spark/Airflow/Delta/Kafka/dbt, with a focus on reliability, data quality and observability.
Practical experience with MLOps (MLflow/Kubeflow/SageMaker/Azure ML) and operationalising ML into CI/CD workflows.
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