Cisco is seeking a highly skilled GenAI/Agentic AI Engineer to work remotely from Egypt with the Cisco team in the US. The ideal candidate will have strong expertise in advanced AI systems, including retrieval-augmented generation (RAG), multimodal models, and agentic workflows. You will play a critical role in designing, developing, and deploying next-generation intelligent systems that leverage generative AI, multi-agent coordination, and real-time learning to solve complex business problems at scale.

Key Responsibilities

AI System Design & Development (Primary)

Design and implement retrieval-augmented generation (RAG) and graph-RAG pipelines.

Build and optimize multimodal models (text, code, vision, voice).

Develop agentic AI systems with tool/function calling and workflow orchestration.

Create and manage multi-agent systems and hierarchical planning architectures.

Integrate hybrid retrieval systems using knowledge graphs and vector databases.

Implement continual and online learning with concept-drift detection.

Ensure robustness through red-teaming, adversarial testing, and guardrail monitoring.

Model Optimization & Deployment

Apply parameter-efficient tuning methods (LoRA, prefix/prompt tuning).

Perform model compression (distillation, quantization, pruning) for efficient edge/on-device deployment.

Implement privacy-preserving ML techniques (federated learning, differential privacy).

Enable secure inference and ensure compliance with data privacy standards.

Integrate models with cloud and on-prem infrastructures using modern MLOps practices.

Evaluation, Explainability & Governance

Develop automated evaluation pipelines for generative model quality and reliability.

Implement explainability and interpretability frameworks (XAI).

Apply provenance, watermarking, and fact-grounding for trustworthy outputs.

Monitor data and model drift to maintain consistent system performance.

Generate and use synthetic data for augmentation and model improvement.

Required Skills & Experience

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• 4+ years of experience in machine learning or AI engineering, with a focus on generative AI or LLMs.
• Strong proficiency with Python and frameworks such as PyTorch, TensorFlow, LangChain, or Hugging Face.
• Hands-on experience with RAG, graph databases (Neo4j, ArangoDB), and vector stores (FAISS, Milvus, Pinecone).
• Deep understanding of multimodal model architectures and agentic AI design.
• Experience with model fine-tuning (LoRA, RLHF, DPO) and orchestration frameworks.
• Familiarity with privacy-preserving ML, federated learning, and secure deployment.
• Strong grasp of cloud environments (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
• Excellent problem-solving, collaboration, an

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