About
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.
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Requirements
- 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 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.
