Vice President - Solutions Engineering

As Vice President - Solutions Engineering at the company, you will lead the technical function that connects AI products, platform capabilities, and applied science to real customer environments. This senior role involves setting technical standards, owning solution strategy, and guiding a team of engineers and architects. You will collaborate with Product, Engineering, and Applied Science teams to translate customer requirements into actionable architectures, ensuring robust and scalable deployments.

Responsibilities

• Lead the Solutions Engineering function across the company’s AI product and platform portfolio, setting the operating model, technical standards, and execution rhythm for customer-facing technical work.
• Own technical solution strategy for strategic customer engagements, from discovery and architecture through validation, deployment planning, and production readiness.
• Partner with Commercial teams to qualify opportunities, shape technical narratives, manage solution complexity, and ensure commitments are grounded in product capability and delivery reality.
• Translate customer requirements, operational constraints, data environments, security needs, and integration patterns into clear solution architectures and implementation paths.
• Work closely with Product and Engineering to feed customer requirements, deployment learnings, product gaps, and technical blockers into roadmap decisions.
• Collaborate with Applied Science teams to explain AI capabilities, model behavior, evaluation approaches, constraints, and trade-offs in ways that are technically accurate and decision-ready.
• Build repeatable mechanisms for demos, proofs of concept, technical workshops, solution design reviews, reference architectures, and deployment handoffs.
• Establish high standards for technical documentation, architecture artifacts, customer-facing materials, validation plans, and solution readiness gates.
• Lead and develop a team of solutions engineers and architects with strong technical judgment, customer credibility, and execution discipline.
• Drive alignment across product, engineering, delivery, security, legal, and customer stakeholders when requirements, timelines, risk, or architecture decisions need clear resolution.
• Improve the quality and repeatability of solution deployment by identifying patterns across customers, reducing bespoke work, and turning field learnings into productized capabilities.
• Represent the company in senior customer and partner conversations where technical depth, product judgment, and deployment credibility are required.

Qualifications

• Senior leadership experience in solutions engineering, sales engineering, customer engineering, solution architecture, technical field engineering, or enterprise AI/product deployment.
• Strong technical depth across AI products, software architecture, data platforms, APIs, cloud infrastructure, security, integration patterns, and production deployment environments.
• Proven ability to lead customer-facing technical teams that operate across discovery, architecture, demos, proof of concept execution, validation, and production handoff.
• Experience working with complex enterprise or national-scale customers where reliability, security, data governance, procurement complexity, and stakeholder alignment matter.
• Strong product judgment, with the ability to distinguish between customer-specific asks, roadmap-worthy patterns, and delivery risks.
• Ability to communicate clearly with executives, engineering teams, applied scientists, commercial teams, and customer technical stakeholders.
• Strong systems thinking, with the ability to connect product capability, customer workflows, technical architecture, delivery effort, operational readiness, and business value.
• Experience building operating models, technical standards, team structures, and repeatable processes for scaling a solutions engineering function.
• High ownership mindset, strong execution discipline, and comfort making decisions in ambiguous, high-stakes environments.
• Experience with AI-native products, LLM applications, agentic systems, RAG architectures, MLOps, model evaluation, or enterprise AI platforms.
• Experience with private cloud, hybrid cloud, on-prem deployment, Kubernetes/OpenShift, observability, identity, access control, or secure integration patterns.