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Databricks

Builds cloud software for data analysis and AI

Applied AI Architect

Pre-Sales Solution EngineeringUpdated on Nov 10
Full-timeRemote in USA

About the Job

Requirements

  • At least 6 years of experience in solutions architecture, technical pre-sales, or a senior hands-on technical role.
  • Experience building and maintaining cloud infrastructure on AWS, Azure, or GCP for production machine-learning applications, including drift monitoring.
  • Experience working with large language models and agentic systems, including vector databases, fine-tuning, AI guardrails, and frameworks such as LangChain, Hugging Face, or OpenAI APIs.
  • Strong coding proficiency in Python and SQL, including live coding, debugging, and solution-building.
  • Expertise in distributed data systems architecture, including scalable pipelines, streaming architectures, lakehouse patterns, and cloud-native data platforms.
  • Proficiency on the Databricks Platform, or demonstrated ability to become proficient rapidly, and a developing technical specialization in an area such as real-time/streaming, ML/AI, data governance, or migrations.
  • Ability to lead architecture discussions with senior technical stakeholders through whiteboarding, design reviews, and trade-off analysis.
  • Experience with production deployments on public cloud (AWS, Azure, or GCP), including infrastructure, security, and governance considerations.
  • Track record of driving platform adoption and consumption growth within accounts.
  • Ability to communicate complex architectures as business value to technical and executive audiences.
  • Bachelor's or master's degree in Computer Science, Engineering, or a quantitative discipline, or equivalent experience.
  • Willingness to travel up to 30% as needed.

Responsibilities

  • Own end-to-end AI/ML technical strategy for accounts, from discovery through production deployment and consumption growth.
  • Lead complex architecture discussions and design scalable, production-grade AI/ML solutions spanning Retrieval-Augmented Generation (RAG), tool calling, multi-agent orchestration, guardrails, AI evaluation, and observability systems.
  • Serve as a trusted technical advisor to customer architects, engineering leads, and directors.
  • Drive technical wins in competitive scenarios by demonstrating Databricks' differentiation through custom-built solutions.
  • Develop and declare an emerging technical specialization (archetype), becoming a go-to resource for the team in that domain.
  • Orchestrate cross-functional resources, including DSAs, SAs, and partners, to deliver comprehensive solutions for complex customer needs.
  • Influence product direction by providing structured feedback on customer requirements and competitive gaps.

Desired Qualifications

  • Databricks certifications in Data Engineer, ML, or Platform.
  • Experience with competitive platforms such as Snowflake, AWS native services, or Azure Synapse, and understanding of the competitive landscape.
  • Background in a data/AI company or cloud provider.
  • Industry domain expertise in areas such as Financial Services, Healthcare, Retail, or Media.

About the Company

Databricks

Builds cloud software for data analysis and AI

Databricks builds cloud software that businesses use to organize data, analyze it, and develop AI applications. Its lakehouse architecture brings the flexible storage of a data lake together with the querying and management tools of a data warehouse. Data engineers prepare and update datasets, analysts build reports, and developers train models or create applications in a shared workspace with controls over who can use the data. The platform runs on Amazon, Microsoft, and Google clouds, and customers pay for computing usage or commit to longer contracts for discounted rates. Databricks was founded in 2013 and is headquartered in San Francisco, California.

Company size
10,001+
Company stage
Late Stage VC
Total funding
$32.1B
Headquarters
San Francisco, CA
Founded
2013

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