Senior Deployment Engineer

Kingston Stanley; Dubai; On-site; AED 300,000 - 460,000; Full time

Job summary

Kingston Stanley is seeking a Deployment Engineering Manager to lead AI-powered deployments from concept to production for enterprise clients.

This hands-on leader will guide deployments across cloud, on-premise and hybrid environments, owning timelines, security and client integrations while building a scaling deployment team as the business grows.

Qualifications

• 7+ years in deployment engineering, implementation or technical delivery.
• Proven experience deploying AI applications into production, including LLMs and RAG pipelines.
• Hands-on with secure AI deployment, GPU infra, model serving and inference optimization.
• Strong knowledge of cloud platforms, APIs, networking and system integration.
• Experience leading complex enterprise deployments and cross-functional teams.
• Understanding of security, privacy and compliance frameworks (ISO 27001, SOC 2, GDPR).
• Bachelor’s degree in CS/Engineering or equivalent experience.

Responsibilities

• Lead end-to-end deployment of AI and enterprise software from 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 config, model serving and AI inference optimization.
• Ensure deployments meet security, data protection and compliance requirements.
• Own deployment timelines, budgets, effort estimation, risk management and quality.
• Develop deployment playbooks, SOPs and technical docs for consistency.
• Collaborate with Engineering, Product, Sales and client stakeholders on scope and readiness.
• Ensure reliable production operations and smooth handovers.
• Build and lead a deployment engineering team as the client base grows.

Skills

Deployment leadership

Cloud deployment across AWS/Azure/GCP

AI production experience

LLMs and RAG pipelines

APIs and system integration

Security & compliance awareness

Project management

Education

Bachelor's degree in Computer Science/Engineering or related field

Tools

AWS

Azure

GCP

Job description

We are seeking a Deployment Engineering Manager to lead the journey of AI-powered products from proof of concept to secure, production-ready deployment for enterprise clients.

This is a hands-on leadership role for someone experienced in deploying AI applications, LLM-based solutions, RAG pipelines and agentic AI systems across cloud, on-premise and hybrid environments.

Working directly with the Founder and alongside the core engineering team, you will own end-to-end deployment delivery, client integrations, production stability, security and technical implementation. You will initially lead deployments hands-on, with the opportunity to build and scale a dedicated deployment engineering team as the business grows.

Key Responsibilities

• Lead end-to-end deployment of AI and enterprise software solutions, 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, including 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.
• Ensure reliable production operations, smooth handovers and continuous improvement.
• Build and lead a deployment engineering team as the client base expands.

Requirements

• 7+ years of experience in deployment engineering, implementation, solutions engineering or technical delivery.
• Proven experience deploying AI applications into production, particularly LLMs, RAG pipelines and agentic AI systems.
• 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.
• 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.
• Proven ability to lead technical teams or cross-functional workstreams.
• Strong 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:

A hands-on technical leader who combines deep deployment expertise with AI production experience, commercial awareness and the ability to build scalable implementation processes in a growing technology business.