Solutions Architect - Public Sector

Cohere Cohere · AI Frontier · Ottawa, ON · Revenue

Solutions Architect for Cohere's Public Sector (Defense and National Security) business, focusing on building and deploying agentic AI solutions using Cohere's foundation models. Responsibilities include architecting scalable NLP/generative AI solutions, collaborating with customers to translate business needs into technical solutions (including fine-tuning and custom agents), supporting LLM deployment in production, and providing customer feedback to product development.

What you'd actually do

  1. Develop and deliver cutting-edge agentic AI solutions utilizing Cohere’s foundation models and Agentic AI Foundry - North.
  2. Architect scalable, secure, and customizable NLP and generative AI solutions tailored to enterprise customer needs.
  3. Collaborate with customers to understand complex workflows, design pilots, and translate business requirements into technical solutions encompassing model fine-tuning, custom agents, and agent orchestration.
  4. Support deployment and integration of large language models (LLMs) and custom solutions into production environments using Kubernetes, Docker, and cloud infrastructures, ensuring high performance and security.
  5. Lead technical engagements, including deep dives into AI architectures, workshop facilitation, and establishing best practices for agent-based AI systems and model customization.

Skills

Required

  • AI/ML solution architecture
  • agentic AI
  • model customization
  • deploying tailored AI models in enterprise contexts
  • Python
  • Jupyter Notebooks
  • Kubernetes
  • Docker
  • cloud managed AI services (AWS Sagemaker, Bedrock, Azure AI Foundry, Google Vertex AI)
  • agent orchestration frameworks
  • custom agents
  • model fine-tuning methodologies
  • generative LLMs
  • communication skills

Nice to have

  • building and managing scalable AI/ML ecosystems
  • multi-cloud deployment strategies
  • security standards for deploying agent-based AI solutions
  • data privacy
  • model safety
  • access controls
  • startup-like context

What the JD emphasized

  • 5+ years of experience in AI/ML solution architecture
  • agentic AI
  • model customization
  • deploying tailored AI models in enterprise contexts
  • Python
  • Jupyter Notebooks
  • Kubernetes
  • Docker
  • cloud managed AI services
  • agent orchestration frameworks
  • custom agents
  • model fine-tuning methodologies
  • AI agents optimized for specific workflows
  • enterprise needs
  • generative LLMs
  • customizing and orchestrating these models

Other signals

  • customer-facing
  • solutions architecture
  • agentic AI
  • LLM customization
  • production deployment