AI Engineer

Latitude; Riyadh; On-site; SAR 420,000 - 660,000; Full time

Job summary

Latitude is seeking an experienced AI systems and platform engineering leader in Riyadh to build production-grade LLM systems, architect multi-agent and RAG workflows, and drive scalable ML pipelines.

The role requires 6–8+ years in software or platform engineering, strong Python, Kubernetes, Terraform, and cloud experience (Azure preferred). You will lead squads, set engineering standards, and deliver safe, measurable AI solutions.

Qualifications

• Bachelor's degree in Software Engineering, Computer Science, or related field.
• 6–8+ years in software, DevOps, or platform engineering, including at least 2 years in an applied AI/ML capacity.
• Proven delivery of production AI/LLM systems, not just research or notebook 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).
• Experience leading a team and setting engineering standards across multiple squads.
• Demonstrated ownership of CI/CD at scale (Azure DevOps, GitHub Actions) and GitOps release models.

Responsibilities

• Build, fine-tune, and evaluate LLM systems for domain-specific tasks (QLoRA / PEFT on open-weight models).
• Design reproducible evaluation harnesses and AB test frameworks with metrics (task success rate, safety rate, latency).
• Architect multi-agent and RAG systems from prototype through production.
• Implement safety guardrails — input validation, allowlist/denylist policies, and risk controls.
• Translate business use cases into deployable prototypes and demo to stakeholders.
• Lead cloud/AI operations team and define monitoring, incident response, and governance.

Skills

Python

Bash

YAML

Kubernetes

Docker/Podman

Terraform

Azure

GitHub Actions

GitOps

CI/CD

Education

Bachelor's degree in Software Engineering or Computer Science

Tools

Milvus

Pinecone

Weaviate

LangGraph

Argo CD

Azure DevOps

Terraform

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.