Solutions Architect (GenAI)

Euna Solutions-posted 3 months ago
Full-time • Senior
501-1,000 employees
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We're seeking a solutions architect to join our GenAI tiger team and design our generative AI platform strategy. This is a foundational role where you'll define the technical architecture that enables AI capabilities across our organization. You'll be part of a fast-moving, cross-functional team driving AI adoption at scale. Given the rapidly evolving nature of GenAI, we value learning agility and systems thinking over specific tool experience.

  • Design scalable, secure GenAI infrastructure that serves multiple business units
  • Define integration patterns between GenAI services and existing enterprise systems
  • Create technical standards and guidelines for AI model deployment and management
  • Architect data flows for training, fine-tuning, and inference pipelines
  • Design MCP (Model Context Protocol) schemas and integration patterns for AI tool connectivity
  • Evaluate and recommend GenAI platforms (cloud providers, model serving infrastructure, vector databases)
  • Design model governance frameworks including version control, A/B testing, and rollback strategies
  • Define observability and monitoring approaches for AI system performance and costs
  • Create disaster recovery and business continuity plans for AI-dependent processes
  • Translate business requirements from product managers into technical architecture
  • Partner with security and compliance teams to ensure AI governance standards
  • Guide engineering teams on implementation patterns and best practices
  • Communicate technical decisions and trade-offs to non-technical stakeholders
  • 7+ years in solutions architecture, platform engineering, or similar technical leadership roles
  • Strong background in cloud infrastructure (AWS/Azure) and distributed systems
  • Experience with API design, microservices architecture, and data pipeline orchestration
  • Track record of building platforms that scale across multiple teams and use cases
  • Experience with Model Context Protocol (MCP) implementation or similar tool integration frameworks
  • Hands-on experience with ML infrastructure, model serving, or data science platforms
  • Familiarity with vector databases, embedding strategies, or search/retrieval systems
  • Experience with containerization, Kubernetes, and DevOps practices
  • Background in regulated industries or environments requiring strong governance
  • Direct experience with LLM APIs, RAG architecture, or prompt engineering automation
  • Knowledge of ML operations (MLOps) tools and practices
  • Understanding of AI safety, bias mitigation, or responsible AI practices
  • Competitive wages
  • Wellness days - an extra day on top of the long weekend twice a year
  • Community Engagement Committee for local community support
  • Flexible work day arrangements
  • Health and dental benefits
  • Culture committee for frequent fun events
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