Senior Services Solutions Architect – AI, Advanced Services
New York · Raleigh · Durham · USD 146,000-219,000 / year · Onsite · Posted Oct 14, 2026
At a glance
- Workplace
- Onsite
- Location
- New York · Raleigh · Durham
- Salary
- USD 146,000-219,000 / year · Base salary
About 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.
- This is a senior, innovation-focused Professional Services role responsible for identifying customer and market opportunities, shaping technical approaches, and maturing successful concepts into scalable services.
- You will act as a senior Advanced Services technical authority for AI, working closely with AI pre-sales teams, Product Engineering, partners, and delivery organizations.
Responsibilities
- 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.
Requirements
- 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.
- 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.
- 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.
Benefits
- Flexible time off
- Wellness resources
- Company-sponsored team events
Workplace
- #LI-ONSITE
Skills
- GenAI
- RAG
- MLOps
- LLMOps
- Kubernetes
- Vector Databases
