About the job
In this role as a Solutions Architect at Cohere, you will play a significant role in growing Cohere’s Defence and National Security business. You will be both a strategic thinker and a hands-on doer, building customer demos and proof of concepts, collaborating with stakeholders, and providing guidance on best practices for using Cohere.
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.
Qualifications
Minimum
- 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.
Preferred
- 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.