Lead AI Engineer

We're looking for a Lead AI Engineer to join our team in Doha, Qatar, in an onsite working mode .

In this role, you will lead the design and implementation of AI-driven strategies, architectures and operating models, enabling engineering teams to transition to spec-driven and agentic AI delivery methods. This is a critical position to influence and drive AI-assisted development, ensuring scalable implementations, governance and measurable business outcomes. Your expertise will shape AI adoption across the organization, balancing innovation with compliance, security and operational integrity.

Architect and deploy token-efficient AI search and retrieval systems optimized for cost, relevance and performance Advise on AI adoption strategies, including when to apply RAG, agents, copilots or traditional engineering approaches Define and implement spec-driven development practices with governance and reusable standards Design KPIs to measure productivity gains, quality improvements and AI adoption maturity Build end-to-end AI pipelines, including embeddings, vector stores, retrieval layers, LLM integrations and streaming inference Develop operational strategies for agentic systems, including rollout governance, versioning and observability Partner with product owners and engineering leads to define AI adoption roadmaps and enable workforce capability uplift Drive training, playbooks and mentorship to accelerate workforce readiness in AI-enabled engineering workflows Implement responsible AI practices, ensuring compliance with security, safety and ethical guidelines Bachelor's degree in Computer Science, Software Engineering or related field 4+ years of experience building production AI systems or 2+ years in production-level LLM/RAG solutions Proven track record leading AI transformations, strategy design and adoption initiatives Deep expertise in RAG architectures: embeddings, vector DBs, retrieval strategies Strong proficiency with Python, familiarity with Java and API protocols (REST/gRPC) Experience with agent frameworks and implementing agentic workflows in enterprise software delivery Practical knowledge of Kubernetes, cloud services, CI/CD pipelines and deployment automation Hands-on experience in integrating LLM providers and self-hosted models into scalable architectures Knowledge of AI observability, testing frameworks, model versioning and prompt governance