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We are seeking a seasoned Generative AI Platforms Architect to define and lead the end-to-end architecture for our enterprise GenAI platform.
You will own the reference architectures, guardrails, and roadmaps that enable secure, scalable, and cost-efficient delivery of AI capabilities—including LLMs, agentic applications, and tool integrations—across multiple clouds. Partnering with engineering, security, data governance, and product teams, you will turn business outcomes into coherent platform designs and incremental delivery plans, while mentoring engineers and championing best practices in LLMOps/ModelOps.
Responsibilities
- Design enterprise generative AI reference architectures, blueprints, and reusable patterns
- Establish multi-cloud platform foundations with solutions for networking, identity, and secrets management
- Provide technical leadership for LLMOps/ModelOps, enabling model evaluation, safety, observability, and rollout strategies
- Define frameworks and governance for agentic systems, including tool governance and Model Context Protocol (MCP)
- Ensure systems adhere to security, risk, and compliance standards, incorporating Responsible AI principles and PII controls
- Develop and enforce strategies for cost efficiency, reliability, and performance across platforms using capacity planning and FinOps techniques
- Drive improvements in the developer experience through golden paths, CI/CD pipelines, templates, and Infrastructure as Code (IaC) methodologies
- Collaborate with cross-functional engineering and product teams to align on system requirements and strategic objectives
Requirements
- Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience
- 1+ years in architecture roles involving cloud, data, or production Generative AI/LLM systems
- Knowledge of cloud platforms (Azure, AWS, GCP) and IaC tools (Terraform, Bicep, CDK, CloudFormation)
- Competency in containerization and orchestration technologies (Docker, Kubernetes) as well as API gateways/service meshes
- Proficiency in CI/CD and release management for ML/LLM workloads (Jenkins, GitHub Actions, GitLab CI, Azure DevOps)
- Understanding of Large Language Model (LLM) platforms such as Azure AI Foundry, Azure OpenAI, AWS Bedrock, or Google Vertex AI
- Familiarity with implementing security-by-design principles and compliance frameworks tailored to GenAI systems
- Advanced proficiency in English (B2+/C1)
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