Forward Deployed AI Engineer – Technical Lead (Dubai, on-site)

Isa Cybersecurity Inc.; Dubai; On-site; AED 400,000 - 700,000; Full time

Job summary

Isa Cybersecurity Inc. in Dubai is seeking a Forward Deployed AI Engineer – Technical Lead to drive hands-on AI squad leadership on-site in the UAE. You will define strategy, own delivery, and build agentic AI systems that deliver measurable outcomes across security contexts.

You will collaborate with stakeholders, architect scalable AI solutions, and guide end-to-end implementation, including data ingestion, model integration, tooling, and governance for enterprise-grade deployments.

Qualifications

• 8+ years of experience building production-grade software with 4+ years in Generative AI/LLMs
• Experience designing and deploying agentic AI solutions
• Hands-on experience with MCP to connect AI agents with systems, tools, APIs and data
• Experience deploying containerized solutions in cloud environments with observability

Responsibilities

• Clarify business problems and define measurable outcomes for AI initiatives
• Lead end-to-end AI engineering efforts from discovery to production deployment
• Design multi-agent systems and tool-orchestration patterns for enterprise scale
• Ensure security, governance, and cost Optimization across AI solutions
• Mentor engineers and establish engineering standards and best practices
• Collaborate with product owners, AI architects and external providers

Skills

Hands-on leadership

AI engineering

Agentic AI

MCP integration

Vector databases

Cloud-native

CI/CD

API design

Tools

MCP (Model Context Protocol)

OpenAPI/REST

Vector search

Kubernetes

Job description

Forward Deployed AI Engineer – Technical Lead (Dubai, on-site)

Europe, Ukraine HOT

About the role

This is an on-site position in the UAE. We are looking for a hands-on technical leader to define the direction of an AI engineering squad embedded within a complex operational business.

You will work directly with stakeholders to understand business challenges and their underlying drivers, then design, build, deploy, and operate agentic AI systems that deliver measurable outcomes. This is a build-first role with end-to-end ownership, combining production engineering, technical leadership, and internal capability development.

What you will do

Understand before you build

• Begin every initiative by clarifying the business problem, objectives, desired outcomes, and how success will be measured.
• Partner directly with stakeholders and lead solutions from discovery and architecture through production deployment, operation, and continuous improvement.
• Challenge assumptions, identify underlying operational drivers, and recognize when AI is not the appropriate solution.
• Design, build, deploy, and continuously improve enterprise-grade agentic AI applications as the primary engineering approach.
• Develop AI agents capable of multi-step reasoning, tool and API orchestration, context management, exception handling, and human-in-the-loop workflows at enterprise scale.
• Implement Retrieval-Augmented Generation solutions, including data ingestion, chunking, vector representations, vector search, retrieval optimization, grounding, and source traceability.
• Connect AI systems to enterprise platforms through Model Context Protocol (MCP), REST APIs, OpenAPI specifications, webhooks, messaging, and event-driven architectures.
• Create reusable, self-service AI services and interfaces that can support multiple business domains.
• Apply structured LLM engineering practices, including tool calling, schema validation, retries, fallbacks, guardrails, and clear recovery paths.

Quality, reliability and governance

• Own solution quality from inception through operation, including testing, evaluation, observability, structured logging, version control, and continuous feedback mechanisms.
• Optimize deployed solutions across accuracy, reliability, performance, security, latency, and cost.
• Apply security, privacy, access-control, auditability, responsible-AI, and governance requirements throughout solution design and operation.

Technical leadership

• Own business outcomes within a defined domain and remain accountable for successful delivery and measurable impact.
• Define technical direction, architecture standards, and build-versus-buy decisions for AI initiatives.
• Establish reusable patterns, frameworks, and engineering standards that strengthen internal AI capabilities.
• Design robust multi-agent systems, agent-to-agent communication patterns, tool-orchestration standards, and evaluation frameworks.
• Work closely with product owners, AI architects, engineers, and business stakeholders across discovery and delivery.
• Lead technical engagements with external technology providers while keeping internal capability development at the center.
• Mentor engineers, conduct technical reviews, and raise standards across quality, security, reliability, and cost optimization.

Required skills and experience

• Demonstrated curiosity and a strong drive to understand complex business and operational challenges before proposing a solution.
• A business-first, human-centered approach that treats AI as a way to augment people rather than replace them.
• Excellent English communication skills and experience working effectively in diverse, international teams.
• 8+ years of experience building production-grade software, including 4+ years with Generative AI, Large Language Models, or applied machine learning and at least 1 year designing and deploying agentic AI solutions.
• Proven success setting technical direction and delivering AI solutions at enterprise scale.
• Hands-on experience or strong working knowledge of MCP for connecting AI agents with systems, tools, APIs, and data sources.
• Practical experience with one or more modern agent orchestration frameworks or enterprise AI platforms.
• Strong understanding of asynchronous programming, API development, typed data validation, CI/CD, testing strategies, source control, logging, and error handling.
• Experience with vector databases or search platforms and production RAG patterns.
• Experience integrating enterprise systems through APIs, managed identities, middleware, webhooks, messaging queues, and cloud-native architectures.
• Practical experience deploying containerized solutions in cloud environments with monitoring and observability.
• Strong judgment across quality, latency, reliability, security, governance, and cost trade-offs.

Will be a plus

• Experience in aviation, transportation, logistics, supply chain, cargo operations, customer service, or other complex operational environments.
• Background in classical machine learning, data science, or advanced analytics.
• Full-stack engineering experience across front-end, API, and back-end systems.
• Experience with Voice AI, email automation, CRM integrations, workflow automation, or multilingual AI solutions.
• Experience building evaluation frameworks, golden datasets, simulation-based testing, regression suites, and AI quality-measurement systems.
• Experience designing multi-agent architectures, agent registries, agent-to-agent communication, and tool-orchestration standards.
• Experience leading delivery with external AI platforms, startups, or technology providers while developing internal engineering teams and capabilities.
• Relevant certifications in cloud AI, Generative AI, agentic AI, MLOps, or related disciplines.

Work model and relocation

The role is full-time and onsite in Dubai, UAE, for an initial one-year assignment with possible extension. The selected candidate will be employed through the designated UAE company. Planned support includes the UAE employment visa, employee medical insurance, an initial flight to Dubai, a return flight at the end of the assignment, an approved broker fee, apartment-search assistance and local arrival support (subject to the final written offer).