Director overseeing the O’Neill Institute for National and Global Health Law at Georgetown University. Leading legal technical assistance and health policy litigation communications.
About the role
- Solution Director managing analytics and AI/ML solutions for Rackspace. Leading presales engagements and strategic direction for Generative AI and data modernization on AWS.
Responsibilities
- 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:
- 1) Generative AI Solutions: Amazon Bedrock implementations, Amazon Q deployments, QuickSight with Q capabilities, RAG architectures, and custom LLM solutions.
- 2) 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.
- 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:
- 1) Executive Level: Amazon Q demonstrations, QuickSight analytics with generative BI, art-of-the-possible sessions.
- 2) 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.
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. 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.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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