AI Engineer

azmtalent; Saudi Arabia; On-site; SAR 240,000 - 360,000; Full time

Job summary

azmtalent is seeking an experienced AI systems leader in Saudi Arabia to build, fine-tune, and evaluate domain-specific LLMs using QLoRA/PEFT on open-weight models. You will architect multi-agent and RAG systems, design evaluation harnesses, and drive production readiness from prototype to deployment.

The role requires 6–8+ years in software/DevOps with hands-on Kubernetes, Terraform, and cloud experience across Azure, OCI, or GCP. Strong leadership and SDLC governance are essential for success.

Qualifications

• Bachelor's degree in software engineering or computer science.
• 6–8+ years in software, DevOps, or platform engineering with AI exposure.
• Proven delivery of production AI/LLM systems, not just research.
• Strong Python; proficient with Bash and YAML.
• Hands-on Kubernetes, Docker/Podman, and Terraform experience.

Responsibilities

• Build, fine-tune, and evaluate LLM systems for domain tasks.
• Architect multi-agent and RAG systems from prototype to production.
• Design reproducible evaluation harnesses and tracked metrics.
• Lead and mentor a cloud/AI operations team with governance.
• Own SDLC standards and platform standardization across squads.

Skills

Leadership

Team mentoring

Engineering standards

Cross-squad coordination

Documentation

Education

Bachelor's degree in Software Engineering or Computer Science

Master's degree in Applied AI (preferred)

Tools

Python

Bash

YAML

Kubernetes

Docker/Podman

Terraform

Azure DevOps

GitHub Actions

Argo CD

Azure Monitor

Application Insights

ELK

LangGraph

Milvus

Pinecone

Weaviate

Job description

AI systems

• Build, fine-tune, and evaluate LLM systems for domain-specific tasks (QLoRA / PEFT on open-weight models such as Llama-3 and Mistral).

• Design reproducible evaluation harnesses and A/B test frameworks with tracked metrics: task success rate, safety rate, and latency distributions (p50/p95).

• Architect multi-agent and RAG systems (LangGraph, FastAPI, vector databases) from prototype through production.

• Implement safety guardrails — input/output validation, allowlist/denylist policies, and controls that reduce invalid or high-risk model actions.

• Translate business use cases into deployable prototypes with measurable acceptance criteria, and demo them to stakeholders.

Platform & infrastructure

• Design and operate cloud infrastructure and MLOps workspaces (Azure, OCI, or GCP) for AI workloads on Kubernetes and containerized runtimes.

• Build CI/CD pipelines and GitOps-based release promotion (Argo CD) across development, test, and production environments.

• Implement end-to-end observability (Azure Monitor, Application Insights, ELK) with defined detection and response targets.

• Apply network and perimeter security baselines (FW/WAF), automated code quality and SCA scanning (SonarQube, Black Duck), and gated pipelines.

• Own disaster recovery design — automated backups, failover, and documented RTO/RPO commitments.

Engineering leadership

• Lead and mentor a cloud/AI operations team; define monitoring, incident response, and release governance practices with clear uptime and MTTR targets.

• Standardize SDLC practices — branching strategy, PR governance, release management, delivery reporting — to improve lead time and deployment frequency.

• Consolidate engineering tooling and workflows; drive migrations and platform standardization where fragmentation slows delivery.

• Produce handover documentation and runbooks that make systems auditable and operationally transferable.

• Support vendor and licensing negotiations for cloud enterprise agreements.

Required Qualifications

• Bachelor's degree in Software Engineering, Computer Science, or a related field.

• 6–8+ years in software, DevOps, or platform engineering, including at least 2 years in an applied AI or ML engineering capacity.

• Proven delivery of production AI/LLM systems — not only research or notebook-stage work.

• Strong Python; comfortable with Bash and YAML.

• Deep hands-on experience with Kubernetes, Docker/Podman, and Terraform.

• Production experience with at least one major cloud (Azure preferred; OCI or GCP acceptable).

• Demonstrated ownership of CI/CD at scale (Azure DevOps, GitHub Actions) and GitOps release models.

• Experience leading a team and setting engineering standards across multiple squads.

Preferred Qualifications

• Master's degree in Applied AI, Machine Learning, or a related discipline.

• Fine-tuning experience with QLoRA/LoRA on GPU clusters; PyTorch and Transformers.

• Vector database experience (Milvus, Pinecone, or Weaviate) and RAG retrieval design.

• Experience delivering on Saudi government or large-scale national digital platforms, with familiarity in local compliance and standards.

• Arabic and English professional proficiency.

Technical Environment

Python, FastAPI, PyTorch, Transformers, LangGraph, 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.