GeAI Engineer

Designs, builds, and maintains end‑to‑end AI orchestration pipelines that automate data ingestion, model inference, and result delivery. Crafts and continuously refines prompts for large language models (LLMs), applying retrieval‑augmented generation (RAG), fine‑tuning techniques, and embedding strategies. Ensures responsible AI practices by implementing model evaluation metrics, guardrails, and compliance checks. Leads incident triage and root‑cause analysis for AI‑driven services, collaborating with data scientists, product owners, and platform engineers to deliver reliable, scalable generative AI solutions.

• Build, test, version, and deploy GenAI models; maintain reproducible pipelines.

• Verify that all code follows ARB’s style guides, security linting, and CI pipelines.
• Ensure that every model artifact includes metadata for provenance, version, and explainability.

Oversee data‑ingestion, cleaning, and labeling workflows; confirm that data‑quality checks are completed before model training.

• Coordinate functional, performance, and bias testing scripts.
• Approve model‑sign‑off packages before they move to staging.

• Trigger CI/CD releases for models, verify successful rollout (canary/blue‑green), and confirm rollback procedures are in place.

• Maintain real‑time monitoring dashboards; review daily/weekly drift reports and initiate retraining tickets when thresholds are breached.

Consistently delivers projects on schedule, within budget, and to quality standards while managing competing priorities.

Builds strong relationships across business, technical, and compliance teams; secures consensus and drives collective ownership of outcomes.

Uses data, risk analysis, and stakeholder input to make timely decisions, even when information is incomplete.

Prioritises the needs of internal and external customers, ensuring solutions are usable, valuable, and meet defined business objectives.

Applies responsible AI principles, proactively identifying bias or compliance concerns and embedding mitigation measures throughout delivery.

Translates complex technical details into concise, business‑oriented language for diverse audiences; facilitates transparent information flow.

Encourages iterative learning, adopts best‑practice methodologies, and seeks efficiencies in model development and operations.

• Bachelor’s degree in Computer Science, Data Science, Engineering or related field;
  Master’s degree preferred.
• 3‑7 years of experience delivering AI/ML solutions
• Proven record of shipping production‑grade GenAI models in a regulated financial environment.
• Strong programming skills (Python, PyTorch/Tensor Flow), experience with large‑language models (LLMs), prompt engineering, fine‑tuning and model‑serving frameworks and voice agent stack (STT/TTS).

• Project & Programme Management:
  Mastery of Agile/Scrum, Kanban, and waterfall hybrids; ability to create realistic schedules, budgets, and resource plans.
• AI/ML Frameworks & Libraries (PyTorch, Tensor Flow, Scikit‑learn, Hugging Face Transformers, Lang Chain).
• Technical Proficiency:
  Hands on experience with large language models (LLMs), prompt engineering, fine tuning, and model compression techniques.
• Ability to define data quality criteria, oversee data labeling workflows, and work with data owners to ensure compliance.
• Governance & Compliance:
  Familiarity with AI model risk frameworks, audit trail requirements, and regulatory guidance (e.g., Saudi Central Bank AI guidelines).
• Financial Acumen:
  Skill in cost benefit analysis, ROI tracking, and optimization of cloud compute spend for AI workloads.