Senior Deployment Engineer - AI Products

About the role

A company seeking a Deployment Engineering Manager to lead AI-powered products from proof of concept to secure, production-ready deployment for enterprise clients. This is a hands-on leadership role working directly with the Founder and alongside the core engineering team, owning end-to-end deployment delivery, client integrations, production stability, security and technical implementation. The candidate starts by leading deployments hands-on, with the chance to build and scale a dedicated deployment engineering team.

Key responsibilities

• Lead end-to-end deployment of AI and enterprise software, from technical discovery to go-live and production support.
• Design and manage deployments across AWS, Azure, GCP, on-premise and hybrid environments.
• Oversee system integrations, APIs, infrastructure configuration, model serving and AI inference optimisation.
• Ensure deployments meet security, data protection and compliance requirements such as ISO 27001, SOC 2, GDPR and the EU AI Act where applicable.
• Own deployment timelines, budgets, effort estimation, risk management and delivery quality.
• Develop deployment playbooks, SOPs and technical documentation to standardise implementation across clients.
• Collaborate with Engineering, Product, Sales and client stakeholders on technical scoping, integrations and production readiness.
• Build and lead a deployment engineering team as the client base grows.

Required profile

• 7+ years of experience in deployment engineering, implementation, solutions engineering or technical delivery.
• Proven experience deploying AI applications into production, especially LLMs, RAG pipelines and agentic AI systems.
• Experience managing complex, client-facing enterprise software deployments across multiple environments.
• Bachelor's degree in Computer Science, Engineering or a related field, or equivalent practical experience.
• Proven ability to lead technical teams or cross-functional workstreams.

Required skills

• Hands-on experience with secure AI model deployment, GPU infrastructure, model serving and inference optimisation.
• Strong knowledge of cloud platforms (AWS, Azure or GCP), APIs, networking, system integration and infrastructure configuration.
• Understanding of security, privacy and compliance frameworks such as ISO 27001, SOC 2, GDPR or the EU AI Act.
• Project management, documentation, stakeholder management and problem-solving skills.