Sr. Specialist Solutions Architect
Databricks•Melbourne, Australia; Sydney, Australia
Full-time7-15
👁️ 0 views•📝 0 applications•Posted 8/12/2026•Expires 10/6/2026
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Job Description
FEQ427R339 Location: Melbourne or Sydney As a Specialist Solutions Architect (SSA), you will be the trusted technical ML & AI expert to both Databricks customers and the Field Engineering organization. You will work with Solution Architects to guide customers in architecting production-grade ML & AI applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform. You will continue to strengthen your technical skills through applying cutting-edge technologies in GenAI, MLOps, and ML more broadly, expanding your impact through mentorship, and establishing yourself as an AI thought leader. The impact you will have: Architect production-level ML & AI workloads for customers using our unified platform, including agents, end-to-end ML pipelines, training/inference optimization, integration with cloud-native services, MLOps, etc. Serve as a trusted practitioner for enterprise GenAI solutions, including RAG architectures, agentic systems (tool-calling agents, multi-agent orchestration, guardrails), natural language querying of structured data, AI evaluation and observability, and monitoring systems Build, scale, and optimize customer AI workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains Provide advanced technical support to Solution Architects during the technical sale, ranging from feature engineering, training, tracking, serving, to model monitoring, all within a single platform, as well as participating in the larger ML SME community in Databricks Collaborate cross-functionally with the product and engineering teams to represent the voice of the customer, define priorities, and influence the product roadmap, helping with the adoption of Databricks’ AI
Required Skills
GenAIMLOpsMLDatabricksCloud-native servicesPythonUnified platformAgentsEnd-to-end ML pipelinesTraining/inference optimizationModel monitoringAI evaluation and observabilityMonitoring systemsRAG architecturesAgentic systemsNatural language querying of structured dataMulti-agent orchestrationGuardrails
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