Manage daily operations of the Retail Team at Allwyn UK to ensure efficient workflow. Act as primary point of contact for the Managing Director's office and support administrative functions.
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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