Solutions Director - Analytics & AI

Rackspace is seeking a highly accomplished Solution Director, Analytics & AI to lead the design and sales of two critical solution portfolios: generative AI/LLM solutions and data modernization/lakehouse architectures on AWS. This pivotal role requires mastery of both domains - leveraging generative AI capabilities (Amazon Q, Amazon Bedrock, QuickSight) to drive executive conversations and opportunity creation, while delivering enterprise data modernization through lakehouse architectures using AWS native services (Glue, SageMaker Unified Studio) and leading platforms (Databricks on AWS, Snowflake on AWS). The position demands cross-functional experience with proven ability to engage C-level stakeholders, drive top-of-funnel opportunity creation, and maintain comprehensive account ownership across the entire customer lifecycle. Responsibilities:

Strategic Leadership & Opportunity Development

Drive top-of-funnel opportunity creation through two parallel tracks: engaging C-level stakeholders with generative AI demonstrations (Amazon Q, Amazon Bedrock) and identifying data modernization needs for lakehouse transformations

Lead the design and architecture of dual solution portfolios:

- Generative AI Solutions, Amazon Bedrock implementations, Amazon Q deployments, QuickSight with Q capabilities, RAG architectures, and custom LLM solutions

- Data Modernization**: Enterprise lakehouse architectures using AWS Glue, SageMaker Unified Studio, Databricks on AWS, and Snowflake on AWS

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

Stay current with advancements in generative AI (foundation models, LLMs) and modern data architectures (lakehouse patterns, data mesh, unified analytics)

Contribute to Rackspace intellectual property through reference architectures covering both generative AI implementations and lakehouse design patterns

Customer Engagement & Solution Delivery

Serve as the primary technical executive orchestrating both generative AI discussions and data modernization programs for strategic accounts

Build strategic relationships using two engagement models:

- Executive Level: Amazon Q demonstrations, QuickSight analytics with generative BI, art-of-the-possible sessions

- Technical Level: Lakehouse architecture workshops, platform assessments (Databricks vs Snowflake vs AWS-native), migration planning

Lead comprehensive consultative engagements that begin with generative AI vision (Amazon Q, Bedrock) and translate into concrete data modernization roadmaps

Develop Statements of Work (SOWs) that balance innovative AI capabilities with foundational data platform requirements

Guide customers through parallel journeys: generative AI adoption (POCs to production) and data platform modernization (legacy to lakehouse)

Collaborate with sales teams positioning both solution portfolios strategically based on customer maturity and needs

Technical Excellence & Market Awareness

Maintain deep expertise across both solution domains:

- Generative AI: Amazon Bedrock, Amazon Q, QuickSight Q, SageMaker JumpStart, prompt engineering, RAG architectures, vector databases

- Data Platforms: AWS Glue, SageMaker Unified Studio, Databricks on AWS, Snowflake on AWS, Redshift, EMR, Apache Iceberg, Delta Lake

Demonstrate comprehensive understanding of how generative AI solutions depend on modern data foundations

Position AWS solutions effectively against other cloud platforms' offerings in both generative AI (Azure OpenAI, Vertex AI) and data platforms (Azure Synapse, BigQuery)

Guide architectural decisions on build vs. buy for both AI capabilities and data platform components

Qualifications and required experience:

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

At least 5 years as a senior-level architect or solutions leader with hands-on experience in both AI/ML and data platform modernization

Demonstrated success 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

Proven ability to articulate both visionary AI possibilities and practical data platform requirements to diverse audiences

Experience with AWS professional services or AWS partner ecosystem across both AI and data domains

Hands-on experience with:

- Multiple lakehouse platforms: Databricks, Snowflake, AWS-native (Glue + Athena + Redshift)

- Multiple AI platforms: AWS Bedrock, Azure OpenAI, Google Vertex AI

AWS: Solutions Architect Professional, Machine Learning Specialty, Data Analytics Specialty

Platform specific: Databricks Certified, Snowflake SnowPro

Experience with regulated industries requiring governance for both AI and data platforms

Track record building practices that deliver both generative AI solutions and data modernization programs

Published thought leadership in generative AI applications and/or modern data architectures

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or related technical field

Advanced degree (Master's or PhD) in a relevant field is highly preferred