Senior Deployment Engineer
Role Overview
The description names the role Deployment Engineering Manager; the source title is Senior Deployment Engineer.
Hands-on leadership role taking AI-powered products from proof of concept to secure, production-ready deployment for enterprise clients.
Work includes AI applications, LLM solutions, RAG pipelines, and agentic AI across cloud, on-premises, and hybrid environments.
Works with the Founder and core engineering team; initially leads deployments hands-on, with an opportunity to build a deployment engineering team.
Key Skills for This Role
Full Job Posting
Role overview
The description names the role Deployment Engineering Manager; the source title is Senior Deployment Engineer.
Hands-on leadership role taking AI-powered products from proof of concept to secure, production-ready deployment for enterprise clients.
Work includes AI applications, LLM solutions, RAG pipelines, and agentic AI across cloud, on-premises, and hybrid environments.
Works with the Founder and core engineering team; initially leads deployments hands-on, with an opportunity to build a deployment engineering team.
Deployment and delivery
- Lead end-to-end AI and enterprise software deployment from technical discovery through go-live and production support.
- Design and manage deployments across AWS, Azure, GCP, on-premises, and hybrid environments.
- Oversee integrations, APIs, infrastructure configuration, model serving, and AI inference optimization.
- Own timelines, budgets, effort estimation, risks, and delivery quality.
- Develop deployment playbooks, SOPs, and technical documentation.
- Coordinate with Engineering, Product, Sales, and client stakeholders on scoping, integrations, and production readiness.
- Maintain reliable production operations, smooth handovers, and continuous improvement; build and lead a deployment engineering team as the business grows.
Required experience and qualifications
- At least 7 years in deployment engineering, implementation, solutions engineering, or technical delivery.
- Production deployment experience with AI applications, especially LLMs, RAG pipelines, and agentic AI systems.
- Hands-on secure AI model deployment, GPU infrastructure, model serving, and inference optimization.
- Cloud platform knowledge (AWS, Azure, or GCP), APIs, networking, integrations, and infrastructure configuration.
- Experience managing complex, client-facing enterprise software deployments across multiple environments.
- Understanding of security, privacy, and compliance frameworks, including ISO 27001, SOC 2, GDPR, or the EU AI Act.
- Ability to lead technical teams or cross-functional workstreams; project management, documentation, stakeholder management, and problem-solving skills.
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Ideal candidate
Hands-on technical leader combining deployment expertise, AI production experience, commercial awareness, and the ability to build scalable implementation processes.
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