Solutions Architect - Public Sector

About the Role

Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We like to work hard and move fast to do what’s best for our customers. Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products. Join us on our mission and shape the future! In this role as a Solutions Architect at Cohere, you will play a significant role in growing Cohere’s Defence and National Security business. In this dynamic role, you will need to be both a strategic thinker and a hands-on doer. You will take a hands-on approach to building customer demos and proof of concepts that showcase the business value of our platform. As the technical relationship owner, you will collaborate with stakeholders to understand their business objectives and translate those into technical solutions. You will take ownership of the customer narrative, serving as the voice of the customer and acting as a liaison between the customer and our product team. Additionally, you will provide guidance on best practices for using Cohere, identify areas for improvement within the platform, and cultivate technical champions within customer organizations to drive adoption and gather feedback to enhance our products.

Responsibilities

• Develop and deliver cutting-edge agentic AI solutions utilizing Cohere’s foundation models and Agentic AI Foundry - North.
• Architect scalable, secure, and customizable NLP and generative AI solutions tailored to enterprise customer needs.
• 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.
• 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.
• Lead technical engagements, including deep dives into AI architectures, workshop facilitation, and establishing best practices for agent-based AI systems and model customization.
• Work with product development to provide customer feedback on agentic AI capabilities, contribute to product enhancements, and help shape future features.

Requirements

• 5+ years of experience in AI/ML solution architecture, with demonstrated expertise in agentic AI, model customization, and deploying tailored AI models in enterprise contexts.
• Strong hands-on skills with Python, Jupyter Notebooks, and cloud-native deployment frameworks such as Kubernetes, Docker, Cloud managed AI services like AWS Sagemaker, Bedrock, or Azure AI Foundry or Google Vertex AI.
• Experience in designing and deploying “agentified” AI workflows, that involve multiple interconnected models or agents, to solve business challenges.
• Hands-on experience building on agent orchestration frameworks like Cohere North and deploying custom agents to production.
• Familiarity with model fine-tuning methodologies, and the development of AI agents optimized for specific workflows and enterprise needs.
• In-depth understanding of the strengths, weaknesses, and operational considerations of generative LLMs, with experience in customizing and orchestrating these models.
• Excellent communication skills to articulate complex AI architectures to both technical stakeholders and executive audiences.

Qualifications

• Background in building and managing scalable AI/ML ecosystems, with knowledge of multi-cloud deployment strategies.
• Familiarity with security standards for deploying agent-based AI solutions, including data privacy, model safety, and access controls.
• Experience working in a startup-like context.