Senior Data & AI Deployment Architect

Databricks

California (MO)

On-site

USD 182,000 - 250,000

Full time

14 days+

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Job summary

Databricks seeks a Forward Deployed Engineer to work with enterprise customers, delivering production-grade data and AI solutions on the Databricks platform. You will own architecture, lead design decisions, and implement end-to-end systems spanning data engineering, ML/AI models, and application development.

You will collaborate with engineering, product, and customer teams to align delivery with business outcomes, travel up to 20%, and demonstrate strong customer empathy in scalable reference

Qualifications

  • 6+ years in data engineering, data platforms, or software engineering.
  • Comfortable coding in Python/Scala/JS/TS and modern frameworks.
  • Experience with two or more cloud ecosystems (AWS/Azure/GCP).
  • Deep experience with distributed computing and Spark.

Responsibilities

  • Lead production-grade solution delivery and reference architectures for data/AI projects.
  • Guide strategic customers through end-to-end design, build, and deployment.
  • Own architecture decisions ensuring security, scalability and alignment with Databricks best practices.
  • Collaborate with engineering, PM, and customer teams to meet engagement goals.
  • Provide feedback to product/support to accelerate issue resolution and adoption.

Skills

Data engineering
Cloud platforms
Python
Scala
JavaScript/TypeScript
Distributed systems
MLOps
Customer-facing

Tools

Apache Spark
CI/CD pipelines
Docker

Job description

Databricks seeks a Forward Deployed Engineer to work with enterprise customers, delivering production-grade data and AI solutions on the Databricks platform. You will own architecture, lead design decisions, and implement end-to-end systems spanning data engineering, ML/AI models, and application development.

You will collaborate with engineering, product, and customer teams to align delivery with business outcomes, travel up to 20%, and demonstrate strong customer empathy in scalable reference

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