Senior AI Engineer; Agentic Systems & Inference - Onsite Job Riyadh Riyadh Region Saudi Arabia,IT/Tech

Position: Senior AI Engineer (Agentic Systems & Inference) - Onsite

Overview

As a Senior AI Engineer, you will be the primary architect of Cognna's autonomous agent reasoning engine and the high-scale inference infrastructure that powers it. You are responsible for building production-grade reasoning systems that proactively plan, use tools, and collaborate. You will own the full lifecycle of our specialized security models, from domain-specific fine-tuning to architecting distributed, high-throughput inference services that serve as Security-core intelligence in our platform.

Responsibilities

Agentic Architecture & Multi-Agent Coordination
Autonomous Orchestration:
Design stateful, multi-agent systems using frameworks like Google ADK
Protocol-First Integration:
Architect and scale MCP servers and A2A interfaces, ensuring a decoupled and extensible agent ecosystem
Cognitive Optimization:
Develop lean, high-reasoning microservices for deep reasoning, optimizing context token usage to maintain high planning accuracy with minimal latency
Model Adaptation & Performance Engineering
Specialized Fine-Tuning:
Lead the architectural strategy for fine-tuning open-source and proprietary models on massive cybersecurity-specific telemetry
Advanced Training Regimes:
Implement Quantization-Aware Training (QAT) and manage Adapter-based architectures to enable the dynamic loading of task-specific specialists without the overhead of full-model swaps
Evaluation Frameworks:
Engineer rigorous, automated evaluation harnesses (including Human annotations and AI-judge patterns) to measure agent groundedness and resilience against the Security Engineer's adversarial attack trees
Production Inference & MLOps at Scale
Distributed Inference Systems:
Architect and maintain high-concurrency inference services using engines like vLLM, TGI, or Tensor

RT-LLM
Infrastructure Orchestration:
Own the GPU/TPU resource management strategy
Observability & Debugging:
Implement deep-trace observability for non-deterministic agentic workflows, providing the visibility needed to debug complex multi-step reasoning failures in production
Advanced RAG & Semantic Intelligence
Hybrid Retrieval Architectures:
Design and optimize RAG pipelines involving graph-like data structures, agent-based knowledge retrieval and semantic searches
Memory Management:
Architect episodic and persistent memory systems for agents, allowing for long-running security investigations that persist context across sessions

Qualifications

Experience:

5+ years in AI/ML Engineering or Backend Systems. Must have contributed to large-scale AI/ML inference service in production
Education:

B.S/M.S. in Computer Science, Engineering, AI, or related fields
Inference Orchestration: KV-cache management, quantization formats like AWQ/FP8, and distributed serving across multi-node GPU clusters
Agentic Development:
Expert in building autonomous systems using Google ADK/Langgraph/Langchain and experienced with AI Observervability frameworks like Lang Smith or Langfuse. Hands-on experience building AI applications with MCP and A2A protocols
Cloud AI Native:
Proficiency in Google Cloud (Vertex AI), including custom training pipelines, high-performance prediction endpoints, and the broader MLOps suite
Programming:
Python and experience with high-performance backends (Go/C++) for inference optimization. You are comfortable working in a Kubernetes-native environment
CI/CD:
Comfortable working in a Kubernetes-native environment

Benefits

Competitive Package - Salary + equity options + performance incentives
Flexible & Remote - Work from anywhere with an outcomes-first culture
Team of Experts - Work with designers, engineers, and security pros solving real-world problems
Growth-Focused - Your ideas ship, your voice counts, your growth matters
Global Impact - Build products that protect critical systems and data

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