13 days ago
Doha, QatarSenior
Responsibilities
- Define technical approaches for AI agents, orchestration frameworks, and responsible AI practices.
- Lead AI solution design, implementation, proof-of-concept development, and architectural decision records.
- Serve as the primary technical advisor on generative AI initiatives and mentor engineering teams on agentic AI patterns.
- Design AI agent systems, multi-agent workflows, tool-use patterns, function calling, MCP, and agent-to-agent communication.
- Architect RAG systems, vector database solutions, embedding and chunking strategies, hybrid search, re-ranking, knowledge graphs, and structured data integrations.
- Lead architecture decisions across Azure AI Foundry, Google Vertex AI, OpenAI APIs, and open-source generative AI models.
- Design prompt management, model routing, fallback, cost optimization, latency management, and scaling strategies for LLM workloads.
- Define and implement evaluation frameworks and automated pipelines for LLM applications and agent systems.
- Establish benchmarks for response quality, factual accuracy, task completion, and user satisfaction.
- Implement responsible AI measures including guardrails, content filtering, output validation, prompt-injection prevention, data-leakage protection, and secure agent execution.
- Ensure compliance with emerging AI regulations and industry standards while evaluating emerging technologies and maintaining technical roadmaps.
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related field; a master's degree is preferred.
- Arabic speaker.
- 8+ years of experience in software engineering or architecture roles.
- 3+ years of hands-on experience building LLM-powered applications in production.
- Deep expertise in AI agent architectures, orchestration patterns, and workflow design.
- Strong experience with at least two of LangChain/LangGraph, Microsoft Semantic Kernel/Agent Framework, OpenAI Agents SDK, and Google Vertex AI Agent Builder.
- Proficiency in Python and async programming patterns.
- Hands-on experience with Azure AI Foundry or Google Vertex AI.
- Strong understanding of generative AI model fundamentals, including prompting, fine-tuning, context windows, and token economics.
- Preferred experience with MCP, Google A2A, multi-agent communication, AI evaluation pipelines, AI quality assurance, advanced RAG, AI observability, AI safety, red-teaming, or responsible AI implementation.
- Ability to design for complexity in agentic systems, communicate AI concepts to diverse audiences, balance innovation with production readiness, learn evolving technologies, and influence across organizational boundaries.
Tech Stack
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