Specialist Solutions Architect - AI/ML

Databricks
Arizona
Workplace: RemoteFull timeUSD 180,000 - 247,500 annuallyFunction: Solutions Engineering & Sales EngineeringExperience: 5+ yearsEducation: mastersSkills: ["Communication","Cross-team collaboration","Continuous learning","Technical thought leadership","Delivering business value"]

Serve as a trusted technical expert in AI/ML for Databricks customers and the Field Engineering organization. Architect production ML/AI workloads on the Databricks platform (including AI agents and MLOps), lead hands-on GenAI implementations like RAG and multi-agent orchestration, and scale solutions across industries. Support the sales cycle with feature engineering and monitoring guidance, then translate customer feedback into actionable product insights.

Loading

Loading job details...

Preparing the role view and application actions.

FursaFursa
Databricks
Databricks
3 days ago

Specialist Solutions Architect - AI/ML

âś“ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 5 hours agoStatus: Live

Job Summary

Serve as a trusted technical expert in AI/ML for Databricks customers and the Field Engineering organization. Architect production ML/AI workloads on the Databricks platform (including AI agents and MLOps), lead hands-on GenAI implementations like RAG and multi-agent orchestration, and scale solutions across industries. Support the sales cycle with feature engineering and monitoring guidance, then translate customer feedback into actionable product insights.
Location: Arizona
Workplace: Remote
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering
Seniority: Mid level

Key Responsibilities

  • •Design and deploy production-level ML and AI architectures on the Databricks unified platform, including AI agents and end-to-end pipeline automation.
  • •Implement enterprise Generative AI solutions hands-on, such as RAG, tool-calling/multi-agent orchestration, guardrails, AI evaluation, and observability.
  • •Build and maintain scalable customer AI workloads using best-practice MLOps across industry domains.
  • •Provide technical pre-sales support during the sales cycle, guiding prospects through feature engineering, model tracking, serving, and monitoring within a single platform.
  • •Collaborate with Engineering and Product teams to translate customer feedback into actionable product insights for Databricks’ AI offerings.
Travel: Medium travel

Pay and Benefits

Salary: USD 180,000 - 247,500 annually
Equity and Bonus:Equity

Key Requirements

  • •5+ years of hands-on industry experience in ML Engineering or AI Engineering, including cloud infrastructure for production ML and drift monitoring or work with LLMs/agentic systems.
  • •Experience with LLMs and agentic systems, including vector databases, fine-tuning, AI guardrails, and frameworks such as LangChain, Hugging Face, or OpenAI APIs.
  • •Ability to communicate complex AI/ML concepts to both technical and non-technical audiences.
  • •Knowledge of modern lakehouse architectures (Delta Lake, data modeling, BI integration) across major clouds (AWS, Azure, or GCP).
  • •Preferred: pre-sales or post-sales technical consulting experience; graduate degree in Computer Science/Engineering/Statistics or equivalent practical experience.
Experience:5+ yearsAI/MLCloud infrastructureLLMsMLOpsLakehouse
Education:Master's in Computer Science, Engineering, Statistics, or related quantitative field
Skills:CommunicationCross-team collaborationContinuous learningTechnical thought leadershipDelivering business value
Languages:English
Tech Stack:Databricks unified platformGenerative AILLMsMLOpsAI agentsPipeline automationModel trainingModel inferenceRetrieval-Augmented Generation (RAG)Tool-callingMulti-agent orchestrationGuardrailsAI evaluationObservability systemsAWSAzureGCPVector databasesFine-tuningLangChain

Company Brief

Databricks
Provides a unified data analytics platform powered by Apache Spark to simplify building, deploying, and scaling data engineering, data science, and machine learning workloads for enterprises.
Industry: Data Infrastructure
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Scaleup
Valuation: Unicorn (USD 1B+)
Funding: Series E+
Headquarters: San Francisco, United States
Founded: 2013
WebsiteLinkedIn