Forward Deployed AI Engineer – Technical Lead; Agentic AI, GenAI & Enterprise AI Solutions UAE

Job Title:

Forward Deployed AI Engineer – Technical Lead (Agentic AI, GenAI & Enterprise AI Solutions) | KBC Technologies | Abu Dhabi, UAE

Recruiting Company: KBC Technologies

Job Location:

Abu Dhabi, United Arab Emirates

Job Type: Full-Time

• Enterprise AI Leadership Role
• Aviation Industry Focus
• Hands-On Technical Leadership Position
• Agentic AI & Generative AI Delivery Environment
• Experience
  
  Required:
  
  8+ Years Software Engineering, 4+ Years AI/ML, 1+ Year Agentic AI

KBC Technologies is seeking a highly experienced Forward Deployed AI Engineer – Technical Lead to design, build, and deploy enterprise-grade Agentic AI solutions within the aviation sector. This role combines hands-on engineering, technical leadership, enterprise architecture, and AI innovation, making it ideal for professionals passionate about delivering production-ready multi-agent AI systems at scale.

As a Forward Deployed AI Engineer – Technical Lead, you will work directly with stakeholders, product teams, architects, and engineering teams to design and implement advanced AI systems that solve real business challenges. You will be responsible for developing enterprise-grade Agentic AI platforms, LLM-powered solutions, retrieval-augmented generation (RAG) systems, and multi-agent workflows using modern AI orchestration frameworks. The role requires deep expertise across Generative AI, cloud-native development, enterprise integrations, vector databases, AI governance, and production deployment practices.

You will lead technical decision-making while remaining actively involved in architecture, coding, prototyping, deployment, and optimization activities. This is an exciting opportunity to shape the future of AI within a large-scale aviation environment.

• Design and deploy enterprise-scale Agentic AI solutions.
• Develop multi-agent systems and autonomous workflow orchestration platforms.
• Build Generative AI applications powered by Large Language Models (LLMs).
• Design and implement Retrieval-Augmented Generation (RAG) architectures.
• Integrate AI services into enterprise applications and operational workflows.
• Develop APIs, backend services, and AI microservices.
• Build scalable cloud-native AI platforms and solutions.
• Implement AI evaluation, monitoring, governance, and observability frameworks.
• Architect and manage vector database implementations and semantic retrieval systems.
• Support enterprise integrations with internal business systems and external services.
• Lead technical design reviews, architecture discussions, and engineering standards.
• Mentor engineers and promote engineering excellence across AI development teams.
• Collaborate closely with business, product, and aviation domain stakeholders.

• Bachelor's Degree in Computer Science, Artificial Intelligence, Software Engineering, Information Technology, Data Science, or a related discipline.
• 8+ years of production software engineering experience.
• 4+ years of experience in Generative AI, Applied AI, Machine Learning, or LLM-based solutions.
• Minimum 1 year of hands-on Agentic AI development experience.
• Strong Python development expertise.
• Proficiency in Type Script, JavaScript, Java, or C#.
• Experience with Model Context Protocol (MCP).
• Deep knowledge of LLM application architecture and deployment.
• Strong experience with enterprise AI solution design and delivery.

• Lang Graph
• Microsoft Semantic Kernel
• CrewAI
• Auto Gen
• OpenAI Agents SDK
• Similar multi-agent orchestration frameworks

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Embeddings
• Vector Databases
• Semantic Search
• AI Evaluation Frameworks
• AI Observability Platforms
• Guardrails and AI Governance Mechanisms

• REST APIs
• Enterprise Integrations
• Microservices Architecture
• Cloud-Native Application Development
• Docker Containers
• Kubernetes
• CI/CD Pipelines
• Dev Ops Practices
• AWS, Azure, or Google Cloud Platforms
• Event-Driven Architectures

• Aviation technology experience.
• Experience deploying enterprise copilots and AI assistants.
• Exposure to AI governance and responsible AI frameworks.
• Knowledge of knowledge graphs and advanced reasoning systems.
• Experience with MLOps and AI platform engineering.

For Agentic AI leadership roles, hiring managers focus on real-world implementation experience rather than experimentation. Highlight projects where you designed and deployed multi-agent systems, implemented RAG architectures, integrated enterprise tools using MCP, orchestrated workflows with Lang Graph or Semantic Kernel, and delivered measurable business outcomes such as automation improvements, operational efficiencies, cost reductions, or productivity gains. Demonstrating both architectural leadership and hands-on engineering expertise is often the strongest differentiator.