GeAI Engineer

1. JOB PURPOSE:
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.

Detailed Tasks:

1.Model Lifecycle Management:

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

2.Technical Standards Enforcement:

– 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.

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

4:Data Prep Supervision:

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

5.Production Deployment Support:

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

6.Performance Monitoring:

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

BEHAVIORAL COMPETENCY FRAMEWORK:
Execution Excellence
Consistently delivers projects on schedule, within budget, and to quality standards while managing competing priorities.
Influence & Collaboration
Builds strong relationships across business, technical, and compliance teams; secures consensus and drives collective ownership of outcomes.
Analytical Decision‑Making
Uses data, risk analysis, and stakeholder input to make timely decisions, even when information is incomplete.
Customer‑Focused Delivery
Prioritises the needs of internal and external customers, ensuring solutions are usable, valuable, and meet defined business objectives.
Ethical AI Stewardship
Applies responsible AI principles, proactively identifying bias or compliance concerns and embedding mitigation measures throughout delivery.
Clear Communication
Translates complex technical details into concise, business‑oriented language for diverse audiences; facilitates transparent information flow.
Continuous Improvement Mindset
Encourages iterative learning, adopts best‑practice methodologies, and seeks efficiencies in model development and operations.

Minimum Qualifications:

– 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/TensorFlow), experience with large‑language models (LLMs), prompt engineering, fine-tuning and model‑serving frameworks and voice agent stack (STT/TTS).

Job-Specific Skills:

– Project & Programme Management: Mastery of Agile/Scrum, Kanban, and waterfall hybrids; ability to create realistic schedules, budgets, and resource plans.
– Programming Languages (Python, SQL , JS (Node js).
– AI/ML Frameworks & Libraries (PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, LangChain).
– 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.