Lead AI Engineer

Were 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

• Bachelors 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