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Where enterprise AI runs and outcomes scale
Lead Solution Architect, Analytics – AI/ML
Location
United States
Posted
106 days ago
Salary
$220K - $269K / year
Seniority
Senior
Job Description
Lead Solution Architect, Analytics – AI/ML
Rackspace Technology
• Lead the design and architecture of dual solution portfolios: Generative AI Solutions and Data Modernization. • Drive top-of-funnel opportunity creation through engaging C-level stakeholders with generative AI demonstrations and identifying data modernization needs. • Act as the trusted advisor, positioning generative AI as the transformational vision while grounding delivery in robust data platform modernization. • Develop compelling business cases that connect AI aspirations with practical data foundation requirements, demonstrating ROI across both portfolios. • Guide customers through parallel journeys: generative AI adoption and data platform modernization. • Maintain deep expertise across both solution domains: Generative AI and Data Platforms.
Job Requirements
- Deep experience with generative AI technologies: Amazon Bedrock, Amazon Q, LLM architectures, RAG implementations.
- Proven track record delivering data modernization: Lakehouse architectures, Databricks and/or Snowflake implementations, AWS Glue/EMR deployments
- A bachelor's degree in computer science, Data Science, Engineering, Mathematics, or a related technical field is required.
- A minimum of 12 years of enterprise solution architecture experience.
- A minimum of 8 years of public cloud experience.
- A minimum of 5 years as a senior-level architect or solutions leader with hands-on experience in both AI/ML and data platform modernization.
- Demonstrated success in engaging C-level executives using generative AI demonstrations while delivering complex data platform transformations.
- Strong understanding across the full spectrum:
- AI/ML: Generative AI, foundation models, LLMs, traditional ML, prompt engineering, fine-tuning.
- Data Platforms: Lakehouse architectures, data mesh, ETL/ELT, streaming, data governance, data quality.
- Proficiency in Python, SQL, and Spark with hands-on experience in:
- Generative AI: LangChain, vector databases, embedding models.
- Data Engineering: PySpark, Apache Iceberg/Delta Lake, orchestration tools.
- A proven ability to articulate both visionary AI possibilities and practical data platform requirements to diverse audiences.
Benefits
- incentive compensation opportunities in the form of annual bonus or incentives
- equity awards
- Employee Stock Purchase Plan (ESPP)
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