Senior Consultant - AI Solution Architect

To define technical approach to AI agents, orchestration frameworks, and responsible AI practices while working closely with engineering teams, product managers, and business stakeholders to bring cutting-edge AI capabilities to production.

 

Lead AI solutions design and implementation
•    Serve as the primary technical advisor on generative AI initiatives
•    Lead proof-of-concept development and architectural decision records
•    Mentor engineering teams on agentic AI patterns and best practices
•    Evaluate emerging technologies and maintain technical roadmaps
•    Design and architect AI agent systems and multi-agent workflows for enterprise use cases
•    Evaluate and implement agent frameworks including LangGraph, Microsoft Agent Framework, OpenAI Agents SDK, Google Agent-to-Agent (A2A), and emerging standard •    Architect tool-use patterns, function calling, MCP, and agent-to-agent communication protocols
•    Design for agent observability, debugging, and human-in-the-loop workflows
•    Design retrieval-augmented generation architectures and architect vector database solutions, embedding strategies, implement chunking strategies, hybrid search, and re-      ranking approaches for optimal retrieval performance
•    Design knowledge graphs and structured data integration with generative AI systems
•    Lead architecture decisions across cloud AI platforms including Azure AI Foundry, Google Vertex AI, OpenAI APIs, and open-source Gen AI models
•    Design prompt management, versioning, and optimization pipelines
•    Architect for multi-model strategies, model routing, and fallback patterns
•    Establish patterns for cost optimization, latency management, and scaling LLM workloads
•    Define comprehensive evaluation frameworks for LLM applications and agent systems
•    Implement automated evaluation pipelines using frameworks such as RAGAS, DeepEval, and custom evaluation harnesses
•    Establish benchmarks for response quality, factual accuracy, task completion, and user satisfaction
•    Champion responsible AI practices including safety, fairness, transparency, and privacy
•    Implement guardrails, content filtering, and output validation systems
•    Design for prompt injection prevention, data leakage protection, and secure agent execution
•    Ensure compliance with emerging AI regulations and industry standards
 

•    Bachelor's degree in Computer Science, Engineering, or related field; Master's 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 the following: LangChain/LangGraph, Microsoft Semantic Kernel/Agent Framework, OpenAI Agents SDK, Google Vertex AI Agent      Builder
•    Proven experience designing and implementing Advanced RAG Systems and Agentic AI
•    Proficiency in Python and experience with async programming patterns
•    Hands-on experience with cloud AI platforms (Azure AI Foundry or Google Vertex AI)
•    Strong understanding of Gen AI models’ fundamentals including prompting, fine-tuning, context windows, and token economics
Preferred Qualifications
•    Experience implementing Model Context Protocol (MCP), Google Agent-to-Agent (A2A) protocol, and multi-agent communication patterns
•    Experience building AI Evals pipelines and AI quality assurance systems
•    Knowledge of advanced RAG techniques: query decomposition, multi-hop reasoning, GraphRAG
•    Experience with AI observability tools 
•    Background in AI safety, red-teaming, or responsible AI implementation.

Skills & Competencies
•    Systems thinking with ability to design for complexity and emergence in agentic systems
•    Strong communication skills to convey AI concepts to diverse audiences
•    Pragmatic approach balancing innovation with production readiness
•    Ability to rapidly learn evolving AI technologies
•    Commitment to ethical AI development and user safety
•    Collaborative mindset with ability to influence across organizational boundaries