Requirements
Experience : 7.5 Years
Mandatory hands-on Python coding capability for building production-grade GenAI ML and RAG pipelines.
Demonstrable experience in building agentic workflows RAG optimizations and scalable LLM integrations.
Strong foundation in traditional ML and data science including feature engineering model tuning and statistical evaluation.
Proven experience using Compass AI services in production-grade projects.
Experience in building and optimizing ML pipelines on Azure.
Good command of the Arabic language (verbal and written) is a plus.
Responsibilities
Lead the engineering deployment and scaling of enterprise-grade AI platforms including Conversational AI LLM-based agents and multi-agent orchestration frameworks.
Design code and implement reusable frameworks APIs and SDKs for scalable enterprise consumption of AI capabilities.
Build production-grade GenAI/RAG/ML systems implementing RAG pipelines vector database integrations and knowledge graph-based retrieval.
Own MLOps for LLM/ML workloads including CI/CD monitoring retraining observability performance tuning and drift detection.
Rapidly translate new research into applied solutions and experiment with emerging tools and techniques.
Evaluate AI frameworks and recommend enterprise adoption strategies.
Mentor and guide AI engineers fostering a culture of experimentation and engineering excellence.
Lead end-to-end delivery of AI projects managing risks related to performance model drift latency and integration.
Ensure engineering quality through regular code and design reviews and coordinate with data scientists platform teams and stakeholders.
Own UAT planning test case development defect triaging and ensure production rollout readiness.
Qualifications :
Bachelors or masters degree in computer science Information Technology or a related field.
Remote Work :
No
Employment Type :
Full-time
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