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

Position Overview

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, Anthropic, 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 in Python, SQL, distributed data processing, and cloud-native ML frameworks.
• Deep experience with Azure ML, Data Factory, Synapse, Data Lake and AWS SageMaker, Glue, S3, Redshift.
• Strong knowledge of LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex).
• Solid understanding of LLM & RAG evaluation metrics (faithfulness, token-F1, citation@k).
• 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.