Senior Services Solutions Architect – AI, Advanced Services

Description

The company is publicly listed on the NYSE and has grown from a storage vendor into a data platform business. It closed fiscal 2026 with $3.7 billion in revenue, including its first quarter above $1 billion, and expects growth to keep accelerating into FY27.

Its strategic focus covers hyperscalers, AI labs, the AI hardware supply chain, data platform providers and the wider AI ecosystem.

The team is building Advanced Services for AI data platforms and production AI, and this is a senior, innovation-led Professional Services role within it.

Responsibilities

• Lead the development of Advanced Services capabilities for AI data platforms and production AI, helping customers turn enterprise data and AI use cases into validated solutions that are ready for production.
• Find customer and market opportunities, shape technical approaches, and test them through customer engagements and pilots.
• Turn approaches that work into scalable, repeatable services and capabilities.
• Act as the senior Advanced Services technical authority for AI, working with AI pre-sales teams, Product Engineering, partners and delivery organisations.
• Combine AI and data architecture skills with hands-on experimentation to guide customers from use-case definition and prototype through data preparation, model and retrieval evaluation, integration and production.
• Work at the point where AI, enterprise data and customer delivery meet. The role is about applying AI to real problems, proving what works, and building the data, architecture and operations needed to run it in production. It is not a general AI application development role or an infrastructure-only role.
• Help define the AI strategy and capabilities for Advanced Services, spotting customer needs, emerging use cases, technology trends and ways to speed adoption.
• Work with strategic customers to understand their AI use cases and convert them into the data, platform and service requirements needed to get from concept to production.
• Design architectures covering enterprise data, preparation and transformation, retrieval, embeddings, vectorisation, knowledge architectures, model integration and AI consumption.
• Build discovery methods, corpus assessments, deployment patterns, reference architectures, sizing guidance, retrieval tuning and production-readiness approaches for the company's data pipeline offering and related AI data pipelines.
• Work alongside customers in an FDE-style model through discovery, prototype, MVP and pilot phases, iterating quickly and turning successful work into delivery capability.
• Help customers move from experiments into production, covering model and retrieval quality, performance, observability, governance, resilience, operational readiness and lifecycle needs.
• Work closely with AI FSAs, AI specialists, Product Engineering, partners and delivery teams to turn emerging AI opportunities into executable customer engagements.
• Feed lessons from real deployments back to Product Engineering and the wider organisation to improve products, architectures and future services.
• Support pipeline growth and strategic deals by turning complex customer needs into credible architectures, pilots and repeatable engagement models.

Requirements

• Proven experience designing, prototyping and deploying enterprise AI/ML or GenAI solutions, and running them beyond proof of concept.
• Strong experience with data preparation, ingestion, transformation, retrieval pipelines, embeddings, vectorisation and modern enterprise data architectures.
• Practical experience with RAG, knowledge graphs, vector databases, chunking, retrieval tuning, model integration and evaluation methods.
• Understanding of how model selection, inference, embeddings, retrieval and evaluation shape enterprise AI data platform design. You do not need to be a model developer or data scientist.
• Understanding of MLOps and LLMOps, including observability, deployment automation, model and data lifecycle management, performance tuning, reliability and operations.
• Solid grasp of the infrastructure behind modern AI, including cloud, Kubernetes, GPU-based architectures, storage and networking. Deep infrastructure engineering expertise is not required.
• Comfort moving between architecture and implementation, building prototypes, diagnosing technical problems and making sound trade-offs when an approach fails.
• Experience working directly with enterprise customers, quickly defining unclear requirements, setting practical delivery paths and taking engagements from discovery through pilot to production.
• Ability to earn the trust of senior technical stakeholders and align Product, Engineering, Sales, Services, partners and customers on one technical direction.
• This role is on-site.

Conditions

Salary ranges depend on role, level and location. For roles open in several locations, the base range reflects the labour market across those locations.

The annual base salary range is $146,000 - $219,000 USD.

The role may be eligible for incentive pay and/or equity.

Applications are accepted on an ongoing basis with no deadline, until the job is filled.

The role is on-site.

Benefits

• Flexible time off, wellness resources and company-sponsored team events.