Principal Solutions Architect, AI Ecosystem & Alliances
Leads strategic AI ecosystem partnerships to design joint architectures for AI infrastructure, spanning data pipelines, model training, and inference serving, while enabling sales teams with technical enablement and market insights.
- Compensation
- Not disclosed
- Location
- U.S. Remote
- Work setup
- Remote
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Architecture
- Posted
Skills
Must have
- 8+ years of experience in alliance management, partner engineering, or technical strategy within enterprise software, cloud, or AI/ML platforms.
- Strong Technical Foundation: Familiarity with LLM deployment stacks, GPU compute, and ML frameworks (PyTorch, TensorFlow, JAX).
- AI Lifecycle Expertise: Experience across the software stack, including fine-tuning, inference serving, and MLOps monitoring.
- Solution Articulation: Proven ability to translate complex technical depth into clear, compelling narratives for both executive and technical audiences.
- Execution: Demonstrated success in executing technology partnerships that result in measurable market impact and revenue growth.
Nice to have
- Cloud-Native Proficiency: Deep experience with OpenShift, Rancher, or Tanzu and Kubernetes-native deployment patterns.
- Modern Data Stack: Familiarity with purpose-built vector stores and their role in RAG architectures.
- Ecosystem Knowledge: Experience with hyperscalers, GPU vendors, and data lakehouse providers.
- Advanced Education: MS/PhD in Computer Science or an MBA is highly desired.
- Community: Involvement with open-source AI communities or standards bodies.
Full description
About The Position
We are seeking a technically fluent Principal Solutions Architect to lead our strategic partnership strategy across the enterprise AI infrastructure ecosystem. You will define joint value propositions that articulate the combined strength of our technology alongside partner platforms, creating differentiated solutions across the full AI stack.
This role sits at the intersection of partnership strategy, technical architecture, and field enablement. You will shape long-term alliance roadmaps, engage credibly with solution architects, and prepare global GTM teams to effectively position joint solutions.
Key Responsibilities
Joint Solution Development & Architecture
- Full-Stack Architecture: Define architectures spanning data ingestion, feature engineering, model training, fine-tuning, and inference serving.
- Technical Integration: Drive certified workstreams across priority areas:
- Enterprise Linux & Containers: Certified integrations with platforms like OpenShift to ensure AI workloads are deployable in hardened environments.
- Orchestration: Develop validated deployment architectures with Kubernetes optimized for AI workloads.
- Vector Databases: Build integrations for semantic search and Retrieval-Augmented Generation (RAG) workflows.
- Inference: Evaluate technical deltas between open source and commercial offerings, authoring defensible PoV on inference stack deployment.
- Engineering Liaison: Coordinate cross-organizational technical resources to produce reference architectures, validated blueprints, and technical PoCs.
- Product Influence: Stay current on inference optimization and retrieval architectures to ensure partner APIs/SDKs are prioritized in the internal product roadmap.
GTM Enablement & Thought Leadership
- Field Readiness: Develop technical battlecards, solution briefs, and demo environments to equip partner and internal sales teams.
- Technical Training: Design and deliver enablement programs for solution engineers to ensure they can confidently deploy joint AI solutions.
- Voice of the Partner: Feed market intelligence and integration feedback back into internal product strategy.
- Industry Presence: Represent the company at conferences and summits; author whitepapers and technical blogs on integrated AI infrastructure.