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's intellectual property through reference architectures covering both generative AI implementations and Lakehouse design patterns. Mentor and provide leadership to Solution Architects by guiding technical development and fostering skill growth across both generative AI and data modernization solution areas. Customer Engagement & Solution Delivery Serve as the primary technical lead 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 proposals 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 to position 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. 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 Al capabilities and data platform components Dual Expertise Required: 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. At the manager’s discretion, additional relevant experience may substitute for the degree requirement. A minimum of 15 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. Proven Presales/Sales Engineering experience. 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. An advanced degree (Master's or PhD) in a relevant field Experience with AWS professional services or AWS partner ecosystem across both Al and data domains Hands-on experience with: Multiple Lakehouse platforms: Databricks, Snowflake, AWS-native (Glue + Athena + Redshift) Multiple Al platforms: AWS Bedrock, Azure OpenAI, Google Vertex Al Industry certifications: 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