Tech Lead – Agentic AI Engineering | Abu Dhabi, UAE

Location: Abu Dhabi, UAE

Work Model: Full-time, On-site

Assignment: Initial 1-year assignment, with possible extension

About the Role

We are looking for a hands-on Tech Lead to set the technical direction for an AI engineering squad embedded in a complex, operational business environment.

This is not a purely managerial role. You will remain deeply involved in architecture, coding, AI solution development, deployment, and technical decision-making while leading the squad's engineering direction.

You will work directly with business and technical stakeholders to understand complex operational challenges, identify the underlying problems, and determine where AI can create measurable value. From there, you will lead solutions from discovery and architecture through development, production deployment, operation, and continuous improvement.

The role combines technical leadership, production software engineering, agentic AI development, and internal capability building.

What You Will Do

Understand the Problem Before Building

• Work directly with business stakeholders to understand challenges, objectives, desired outcomes, and success metrics.
• Challenge assumptions and identify the underlying operational drivers behind a problem.
• Determine whether AI is genuinely the right solution rather than defaulting to an AI-based approach.
• Own initiatives end-to-end, from discovery and architecture through deployment, operation, and continuous improvement.

Build Agentic AI Solutions

• Design, build, deploy, and continuously improve enterprise-grade agentic AI applications.
• Develop AI agents capable of multi-step reasoning, tool and API orchestration, context management, exception handling, and human-in-the-loop workflows.
• Design and implement Retrieval-Augmented Generation (RAG) solutions, including data ingestion, chunking, embeddings/vector representations, vector search, retrieval optimization, grounding, and source traceability.
• Integrate AI systems with enterprise platforms using Model Context Protocol (MCP), REST APIs, OpenAPI, webhooks, messaging, and event-driven architectures.
• Build reusable AI services and interfaces that can support multiple business domains.
• Apply robust LLM engineering practices including tool calling, schema validation, retries, fallbacks, guardrails, and recovery mechanisms.

Own Quality, Reliability & Governance

• Own solution quality throughout the entire lifecycle, from initial design through production operation.
• Establish testing and evaluation approaches for AI systems, including observability, structured logging, version control, and continuous feedback.
• Optimize solutions across accuracy, reliability, performance, latency, security, and cost.
• Embed security, privacy, access control, auditability, responsible AI, and governance requirements into solution design and operation.

Provide Technical Leadership

• Define technical direction, architecture standards, and build-versus-buy decisions for AI initiatives.
• Establish reusable engineering patterns, frameworks, and standards that strengthen internal AI capabilities.
• Design robust multi-agent architectures, 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 maintaining a strong focus on developing internal engineering capabilities.
• Mentor engineers, conduct technical reviews, and raise standards across engineering quality, security, reliability, and cost optimization.
• Own measurable business outcomes within the assigned domain.

Required Skills & Experience

Engineering & AI

• 8+ years of experience building production-grade software.
• 4+ years of experience with Generative AI, Large Language Models, applied machine learning, or related AI technologies.
• At least 1 year of hands-on experience designing and deploying agentic AI solutions.
• Proven experience setting technical direction and delivering AI solutions at enterprise scale.
• Strong Python programming skills.
• Strong proficiency in at least one additional programming language:
• TypeScript / JavaScript, Java, C#
• Hands-on experience or strong working knowledge of Model Context Protocol (MCP).
• Practical experience with 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
• Error handling

AI Architecture & Integration

• Production experience with vector databases or search platforms and RAG architectures.
• Experience integrating enterprise systems through APIs, managed identities, middleware, webhooks, messaging queues, and cloud-native architectures.
• Practical experience deploying containerized applications in cloud environments.
• Experience with monitoring, observability, and production operations.
• Strong technical judgment across quality, latency, reliability, security, governance, and cost trade-offs.

Leadership & Communication

• Demonstrated curiosity and a strong drive to understand complex business and operational problems before proposing technical solutions.
• Business-first and human-centered approach to AI, with a focus on augmenting people and improving outcomes.
• Proven ability to lead technical direction while remaining hands-on.
• Excellent English communication skills.
• Experience working effectively within diverse, international teams.

Nice to Have

Experience in any of the following would be an advantage:

• Aviation
• Transportation
• Logistics
• Supply chain
• Cargo operations
• Customer service
• Other complex operational environments

Additional valuable experience includes:

• Classical machine learning, data science, or advanced analytics.
• Full-stack engineering across front-end, API, and back-end systems.
• Voice AI, email automation, CRM integrations, workflow automation, or multilingual AI.
• Building AI evaluation frameworks, golden datasets, simulation-based testing, regression suites, and AI quality measurement systems.
• Multi-agent architectures, agent registries, agent-to-agent communication, and tool-orchestration standards.
• Leading delivery with external AI platforms, startups, or technology providers while building internal engineering capability.
• Relevant certifications in cloud AI, Generative AI, agentic AI, MLOps, or related disciplines.

Work Model & Relocation

This is a full-time, on-site position in Abu Dhabi, UAE.

The initial assignment is for one year, with the possibility of extension.

The selected candidate will be employed through the designated UAE company.

Planned relocation and employment support includes:

• UAE employment visa
• Employee medical insurance
• Initial flight to the UAE
• Return flight at the end of the assignment
• Approved broker fee
• Apartment-search assistance
• Local arrival and relocation support

All benefits are subject to the final written offer and applicable company policies.

Annual Leave & Sick Leave

• 30 days of paid annual leave per year, which may be taken in parts.
• Following the three-month probation period, employees are entitled to up to 90 days of sick leave per year:
• 15 days at full pay
• 30 days at half pay
• 45 days unpaid
• Sick leave during probation is unpaid.
• Sick leave requires an official medical certificate submitted within three working days.

Who This Role Is For

This role is particularly suited to an engineer who enjoys being close to both the technology and the business problem.

You should be comfortable moving between:

Business problem → AI architecture → hands-on development → production deployment → technical leadership → measurable business outcome.

If you are an experienced software engineer who has moved deeply into Generative AI and agentic systems—and you want to lead technically without stepping away from the engineering work—this could be a strong fit.