Senior AI Engineer (Agentic Systems & Inference)

COGNNA; Saudi Arabia; On-site; SAR 200,000 - 300,000; Full time

Job summary

A leading AI solutions provider in Saudi Arabia is seeking a Senior AI Engineer to architect an autonomous agent reasoning engine and a high-scale inference infrastructure. The role includes designing multi-agent systems, optimizing AI solutions, and contributing to large-scale inference services. Candidates should have over 5 years of experience in AI/ML engineering and be proficient in Python and Google Cloud. A competitive package is offered, along with opportunities for growth and impactful work.

Benefits

Competitive Package – Salary + equity options + performance incentives

Onsite Experience – Work from our office in Riyadh, KSA

Team of Experts – Work with industry professionals

Growth-Focused – Your ideas ship, your voice counts

Global Impact – Build products that protect critical systems

Qualifications

• 5+ years of experience in AI/ML Engineering.
• Contributed to large-scale AI/ML inference service in production.
• Hands-on experience with MCP and A2A protocols.

Responsibilities

• Design stateful multi-agent systems.
• Architect and scale MCP servers and A2A interfaces.
• Optimize context token usage for high planning accuracy.

Skills

Experience in AI/ML Engineering

Backend Systems knowledge

Expert in Google ADK

Proficiency in Python

Familiarity with cloud platforms

Comfortable in Kubernetes

Education

Bachelor's or Master's in Computer Science

Tools

Google Cloud (Vertex AI)

TensorRT-LLM

Go/C++

Job description

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 TensorRT-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.

Requirements / 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 Compuper 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 LangSmith 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: You are comfortable working in a Kubernetes-native environment.

Benefits

• Competitive Package – Salary + equity options + performance incentives
• Onsite Experience – Work from our office in Riyadh, KSA
• 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