THE ROLE

Everpure is looking for a Senior Services Solution Architect to lead the development of Advanced Services capabilities across AI data platforms and production AI, helping customers turn enterprise data and AI use cases into validated, production-ready solutions.

This is a senior, innovation-focused Professional Services role responsible for identifying customer and market opportunities, shaping technical approaches, validating them through customer engagements and pilots, and maturing successful concepts into scalable, repeatable services and capabilities.

You will act as a senior Advanced Services technical authority for AI, working closely with our AI pre-sales teams, Product Engineering, partners, and delivery organizations. You will combine strong AI and data architecture expertise with hands-on experimentation to help customers progress from use-case definition and prototype through data preparation, model and retrieval evaluation, integration, and production.

The role sits at the intersection of AI, enterprise data and customer execution. It is not a generic AI application-development role, nor an infrastructure-only role. The focus is applying AI to real customer problems, proving what works, and establishing the data, architecture and operational foundations required to run it successfully in production.

What You'll Do

• Shape AI Services Strategy: Help define the Advanced Services strategy and capabilities for AI, identifying customer needs, emerging use cases, technology trends, and opportunities to accelerate adoption.
• Validate AI Requirements: Work with strategic customers to understand AI use cases and translate them into the data, platform and services requirements necessary to move from concept to production.
• Architect AI Data Solutions: Design architectures spanning enterprise data, preparation and transformation, retrieval, embeddings, vectorization, knowledge architectures, model integration, and AI consumption.
• Drive DataStream Services: Develop discovery methodologies, corpus assessment, deployment patterns, reference architectures, sizing, retrieval optimization and production-readiness approaches around DataStream and associated AI data pipelines.
• Lead Pilot-Led Innovation: Work in an FDE-style model with customers through discovery, prototype, MVP and pilot phases, iterating rapidly and turning successful approaches into scalable delivery capability.
• Productionize AI Solutions: Help customers move from experimentation into production, addressing model and retrieval quality, performance, observability, governance, resiliency, operational readiness, and lifecycle requirements.
• Bridge Pre-Sales and Delivery: Partner closely with AI FSAs, AI specialists, Product Engineering, partners, and delivery teams to transition emerging AI opportunities into executable customer engagements.
• Influence Product and Services Direction: Capture learning from real customer deployments and create feedback loops into Product Engineering and the wider organization to improve products, architectures, and future services.
• Accelerate Services Growth: Influence pipeline and strategic opportunities by translating complex customer requirements into credible architectures, pilots, and repeatable engagement models.
  
  

What You Bring

• AI Expertise: Proven experience designing, prototyping, and deploying enterprise AI/ML or GenAI solutions, with the ability to operate beyond proof-of-concept environments.
• AI & Data Architecture: Strong experience across data preparation, ingestion, transformation, retrieval pipelines, embeddings, vectorization, and modern enterprise data architectures.
• GenAI and Retrieval Expertise: Practical experience with RAG, knowledge graphs, vector databases, chunking strategies, retrieval optimization, model integration, and evaluation methodologies.
• Model & Retrieval Understanding: Strong understanding of how model selection, inference, embeddings, retrieval and evaluation affect the design of enterprise AI data platforms. You do not need to be a model developer or data scientist.
• Production AI Experience: Understanding of MLOps/LLMOps, observability, deployment automation, model and data lifecycle management, performance tuning, reliability, and operationalization.
• Modern AI Platform Awareness: Strong understanding of the infrastructure and platforms supporting modern AI environments, including cloud, Kubernetes, GPU-enabled architectures, storage, and networking. Deep infrastructure engineering expertise is not required.
• Hands-On Technical Credibility: Ability to move comfortably between architecture and implementation, prototype solutions, diagnose technical challenges, and make sound trade-offs when an initial approach does not work.
• Professional Services Experience: Experience working directly with enterprise customers, rapidly characterizing ambiguous requirements, defining practical delivery paths, and progressing engagements from discovery through pilot and production.
• Cross-Functional Technical Leadership: Ability to establish credibility with senior technical stakeholders and bring together Product, Engineering, Sales, Services, partners, and customers around a common technical direction.

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