Analytics and AI Solution Architect

Intel Intel · Semiconductors · Arizona, Phoenix, United States +3

This role focuses on developing and implementing cloud-native analytical applications and solutions, with a strong emphasis on leveraging data engineering and cloud platforms. A key aspect involves designing and building pipelines for structured and unstructured data, including the use of Vector databases within a Retrieval Augmented Generative AI architecture. The role also involves applying Lambda architecture, database design, and data modeling, and collaborating on AI/ML infrastructure and big data integrations.

What you'd actually do

  1. Develop and implement cloud-native analitic applications and solutions using industry-leading cloud platforms such as Amazon Web Services, Google Cloud, and Azure.
  2. Design and coordinate cloud architecture across areas including application development, identity and access management, network and data management, and security.
  3. Define DevSecOps solutions and integrate them with cloud platforms, utilizing DevOps processes, automation, and target platforms.
  4. Build and design web services for cloud environments, orchestrating and automating cloud-based platforms.
  5. Leverage data engineering expertise to build pipelines for structure and non-structure data and design databases using tools such as Databricks, Snowflake, NoSQL, and Vector databases in a Retrieval Augmented Generative AI architecture.

Skills

Required

  • AWS
  • Google Cloud
  • Azure
  • web service design
  • orchestration
  • database engineering
  • data modeling
  • Databricks
  • Snowflake
  • NoSQL
  • Vector databases
  • Lambda architecture
  • DevSecOps
  • HTML
  • JavaScript libraries
  • agile methodology

Nice to have

  • AI/ML infrastructure solutions
  • agentic AI
  • MCP
  • Spark
  • Splunk

What the JD emphasized

  • Retrieval Augmented Generative AI architecture
  • Vector databases
  • Lambda architecture
  • DevSecOps

Other signals

  • Leverage data engineering expertise to build pipelines for structure and non-structure data and design databases using tools such as Databricks, Snowflake, NoSQL, and Vector databases in a Retrieval Augmented Generative AI architecture.
  • Apply Lambda architecture, database design, and data modeling to support scalable and efficient cloud-based applications.
  • Collaborate with multidisciplinary teams to deliver AI/ML infrastructure solutions and big data cloud integrations, such as Spark and Splunk.