AI Engineer

AI Engineer – Drive Innovation in Large-Scale Language Model Solutions

Are you passionate about shaping the future of artificial intelligence through cutting-edge, production-grade systems? We are seeking a highly skilled AI Engineer to lead the design, development, and deployment of advanced AI solutions, including fine-tuned large language models (LLMs) and multi-agent systems. This role demands a blend of technical expertise, strategic vision, and leadership to architect scalable, secure, and high-performance AI platforms that deliver measurable business impact.

In this dynamic position, you will collaborate with cross-functional teams to translate complex business requirements into robust, deployable AI prototypes. Your work will span the entire AI lifecycle—from model fine-tuning and evaluation to system integration, observability, and governance—ensuring seamless scalability and compliance with enterprise standards. You will also play a pivotal role in mentoring and guiding a team of cloud and AI operations engineers, fostering a culture of excellence in software development, DevOps, and MLOps practices.

Key Responsibilities

Your responsibilities will include, but are not limited to:

• AI System Development and Optimization: Build, refine, and evaluate domain-specific LLM systems using advanced techniques such as QLoRA and PEFT on open-weight models like Llama-3 and Mistral. Design reproducible evaluation frameworks and A/B testing methodologies to measure performance metrics, including task success rates, safety compliance, and latency distributions (p50/p95).
• Multi-Agent and RAG System Architecture: Develop sophisticated multi-agent systems and Retrieval-Augmented Generation (RAG) pipelines using tools like LangGraph, FastAPI, and vector databases. Scale these systems from prototype to production, ensuring high availability, reliability, and performance.
• Safety and Compliance Guardrails: Implement rigorous input/output validation, allowlist/denylist policies, and automated controls to mitigate risks associated with model outputs. Ensure alignment with organizational safety standards and regulatory requirements.
• Prototyping and Stakeholder Engagement: Translate business use cases into functional prototypes with clear acceptance criteria. Present and demonstrate these solutions to stakeholders, driving adoption and continuous improvement.
• Cloud and MLOps Infrastructure: Design and manage cloud-based AI infrastructure on platforms like Azure, OCI, or GCP, leveraging Kubernetes and containerized runtimes. Optimize MLOps workflows, including CI/CD pipelines, GitOps-based deployments (Argo CD), and automated release promotion across environments.
• Observability and Security: Establish end-to-end observability using tools like Azure Monitor, Application Insights, and ELK. Implement network security baselines, automated code quality scanning (SonarQube, Black Duck), and compliance checks to ensure robust system resilience.
• Disaster Recovery and High Availability: Lead the design of disaster recovery strategies, including automated backups, failover mechanisms, and documented recovery time objectives (RTO) and recovery point objectives (RPO).
• Engineering Leadership: Mentor and lead a team of cloud/AI operations engineers, defining best practices for monitoring, incident response, and release governance. Standardize SDLC processes, including branching strategies, PR governance, and release management, to enhance delivery efficiency.
• Tooling and Workflow Optimization: Consolidate engineering tooling and workflows, driving migrations and platform standardization to eliminate fragmentation and accelerate delivery. Develop comprehensive handover documentation and runbooks for operational continuity.
• Vendor and Licensing Negotiations: Support negotiations for cloud enterprise agreements and licensing, ensuring cost-effectiveness and alignment with organizational goals.

Qualifications and Experience

To excel in this role, you must possess:

• Education: A Bachelor’s degree in Software Engineering, Computer Science, or a related field. A Master’s degree in Applied AI, Machine Learning, or a related discipline is highly preferred.
• Technical Proficiency: 6–8+ years of experience in software, DevOps, or platform engineering, with at least 2 years in applied AI or ML engineering. Proven track record of delivering production AI/LLM systems—beyond research or notebook-stage experimentation.
• Programming Languages: Proficiency in Python with experience in Bash and YAML. Familiarity with PyTorch and the Hugging Face Transformers library is a significant advantage.
• Cloud and Containerization: Hands-on experience with Kubernetes, Docker/Podman, and Terraform. Strong production experience with at least one major cloud provider, with a preference for Azure (OCI or GCP also acceptable).
• CI/CD and GitOps: Demonstrated ownership of CI/CD pipelines at scale, including Azure DevOps, GitHub Actions, and GitOps release models like Argo CD.
• Leadership and Collaboration: Experience leading a team and establishing engineering standards across multiple engineering squads. Ability to drive cultural change and improve lead time and deployment frequency.
• Preferred Specializations: Fine-tuning experience with QLoRA/LoRA on GPU clusters. Expertise in vector databases (Milvus, Pinecone, or Weaviate) and RAG retrieval design. Familiarity with delivering solutions for large-scale national digital platforms, including compliance with local standards.
• Language Proficiency: Professional proficiency in both Arabic and English.

Technical Environment

You will work within a robust technical stack, including:

• Python, FastAPI, PyTorch, Transformers, LangGraph
• Vector databases (Milvus, Pinecone, Weaviate)
• Redis, PostgreSQL
• Kubernetes, Docker/Podman, Terraform
• Argo CD, Azure DevOps, GitHub Actions
• Azure ML, Azure Monitor, Application Insights, ELK
• SonarQube, Black Duck, Fortinet FW/WAF

This is an opportunity to make a lasting impact on AI-driven innovation while leading a team of talented engineers toward excellence. If you are ready to take on this challenge and drive transformative AI solutions, we invite you to apply.