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Data Scientist – Agentic RAG & LLM (Databricks / Azure / AWS) - Australia & New Zealand Singapore Job Opening In Singapore, Singapore – Now Hiring Rhino Partners Pte Ltd

Data Scientist – Agentic RAG & LLM (Databricks / Azure / AWS) Australia & New Zealand Singapore

    Singapore Jobs Expertini Expertini Singapore Jobs It & Technology Data Scientist – Agentic Rag & Llm (Databricks / Azure / Aws) Australia & New Zealand Singapore

Job description

Location: Australia & New Zealand (candidates must have valid working rights in either country)

Position Overview

We are seeking a highly skilled Data Scientist with strong expertise in Databricks, Azure, and AWS, specializing in Agentic Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) .

The role focuses on designing and productionizing intelligent AI/ML systems with scalable, cloud-native deployments, CI/CD pipelines, and MLOps best practices.

The ideal candidate is hands-on, solution-oriented, and experienced in building and deploying advanced AI systems across multiple cloud platforms.

Key Responsibilities

  • Design and implement Agentic RAG pipelines using Databricks Vector Search, MLflow, Unity Catalog, integrated with Azure Cognitive Search and AWS OpenSearch.

  • Develop agent-based workflows using LangChain, LangGraph, LlamaIndex, and other tool-augmented reasoning frameworks.

  • Fine-tune, evaluate, and deploy LLMs (OpenAI, Anthrop ic, MosaicML, Hugging Face, Llama) for enterprise applications.

  • Build CI/CD pipelines for ML & GenAI workloads, including:
    • Automated build/test/deploy workflows (Azure DevOps, GitHub Actions, Jenkins, AWS CodePipeline).

    • MLflow model registry integration with production/staging environments.

    • Infrastructure-as-Code (IaC) using Terraform, Bicep, or CloudFormation for reproducible deployments.

  • Implement MLOps best practices : experiment tracking, versioning, continuous evaluation, automated retraining pipelines.

  • Ensure data governance, compliance, and security for sensitive datasets across Azure and AWS.

  • Collaborate with engineering and product teams to integrate ETL/ELT pipelines in Azure Data Factory, Synapse, AWS S3, Redshift, Glue.

  • Deploy and monitor models with online evaluation pipelines (MLflow Evaluate, DeepEval, custom scorers such as faithfulness, retrieval recall).

  • Provide technical mentorship on GenAI architecture, CI/CD, and production-grade LLM deployments .

Required Skills & Qualifications

  • Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, or related fields (PhD optional, not mandatory).

  • 4+ years of professional experience delivering ML/AI or data science solutions, including cloud-native deployments.

  • Strong expertise with the Databricks ecosystem : Spark (PySpark/Scala), Delta Lake, Unity Catalog, MLflow, Vector Search.

  • Hands-on experience with CI/CD pipelines for ML and GenAI:
    • Azure DevOps, GitHub Actions, or Jenkins.

    • Automated testing for ML pipelines.

    • Model promotion workflows (dev → staging → prod).

  • Proficiency inPython, SQL, distributed data processing, and cloud-native ML frameworks .

  • Deep experience withAzure ML, Data Factory, Synapse, Data Lake andAWS SageMaker, Glue, S3, Redshift .

  • Strong knowledge of LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex).

  • Solid understanding of LLM & RAG evaluation metrics (faithfulness, token-F1, ).

  • Must have valid working rights in Australia or New Zealand .

Preferred Qualifications

  • Experience deploying multi-agent LLM systems in production.

  • Familiarity with Infrastructure-as-Code (Terraform, Bicep, CloudFormation) for CI/CD automation.

  • Hands-on experience with containerization and orchestration (Docker, Kubernetes, AKS, EKS).

  • Contributions to open-source GenAI/LLM projects or published research.

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Required Skill Profession

It & Technology


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