Technical Architect, AI/ML

Snowflake

Technical Architect for AI/ML solutions at Snowflake, focusing on customer engagement and data science pipelines.

Posted 7/14/2026full-timeSydney • 🇦🇺 AustraliaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Snowflake technology for AI/ML workloads, including building and deploying ML pipelines, and possesses a thorough understanding of the Data Science life-cycle and MLOps methodologies. Capable of presenting technical concepts to diverse audiences and collaborating with cross-functional teams to enhance product offerings.

Highest-signal resume keywords

Snowflake ExpertiseMLOps UnderstandingData Science Life-Cycle KnowledgeSQL and Python ScriptingPublic Cloud Platform Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonMLOpsData ScienceFeature EngineeringModel DevelopmentModel DeploymentModel ManagementLarge Language ModelsData Science Tools

Soft Skills

Presentation SkillsCollaborationCustomer EngagementKnowledge TransferTechnical Guidance

Tools & Technologies

SnowflakeAWSAzureGCPSagemakerAzureMLVertexDataikuDataRobotJupyter Notebooks

Industry Keywords

AI/ML WorkloadCompetitive TechnologiesComplementary TechnologiesSystem IntegratorTechnical Challenges

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaPandasPythonPyTorchScalaScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Be a technical expert on all aspects of Snowflake in relation to the AI/ML workload
Build, deploy and ML pipelines using Snowflake features and/or Snowflake ecosystem partner tools based on customer requirements
Work hands-on where needed using SQL, Python, and APIs to build POCs that demonstrate implementation techniques and best practices on Snowflake technology for GenAI and ML workloads
Follow best practices, including ensuring knowledge transfer so that customers are properly enabled and are able to extend the capabilities of Snowflake on their own
Maintain deep understanding of competitive and complementary technologies and vendors within the AI/ML space, and how to position Snowflake in relation to them
Work with System Integrator consultants at a deep technical level to successfully position and deploy Snowflake in customer environments
Provide guidance on how to resolve customer-specific technical challenges
Support other members of the Services Delivery team develop their expertise
Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing

Requirements

What you’ll need

Minimum 10 years experience working with customers in a pre-sales or post-sales technical role
Skills presenting to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos
Thorough understanding of the complete Data Science life-cycle including feature engineering, model development, model deployment and model management.
Strong understanding of MLOps, coupled with technologies and methodologies for deploying and monitoring models
Experience and understanding of at least one public cloud platform (AWS, Azure or GCP)
Experience with at least one Data Science tool such as Sagemaker, AzureML, Vertex, Dataiku, DataRobot, H2O, and Jupyter Notebooks
Experience with Large Language Models, Retrieval and Agentic frameworks
Hands-on scripting experience with SQL and at least one of the following; Python, R, Java or Scala.
Experience with libraries such as Pandas, PyTorch, TensorFlow, SciKit-Learn or similar
University degree in computer science, engineering, mathematics or related fields, or equivalent experience

Benefits

Comp & perks

Ability and flexibility to travel to work with customers on-site 25% of the time