AI Platform Principal

AI Platform Principal

Location - Quasar Israel Hayetzira 6 Yokneam Ilit 2060101 Israel

Role Overview

As Quasar’s first AI Platform Principal, you will establish Quasar’s AI framework and drive enterprise‑wide implementation of the AI Work Intelligence Platform, fine‑tune its agents for Quasar‑specific processes and build internal capabilities so teams can iterate with minimal vendor reliance. Success in this role hinges on your deep understanding of large language models (LLMs) and your ability to build effective tooling in low‑code environments.

Key Responsibilities

Platform Advocacy

• Champion AI platform implementation across all functions of the company.
• Host demos, roadshows and AI “office hours”; create playbooks and case studies.

MLOps & Performance

• Build dashboards for model accuracy, latency and cost; automate retraining loops and internal benchmarking.
• Own release management, rollback plans and validation protocols.

System Configuration

• Perform light configuration of the platform, such as adjusting role‑based access controls, workflow parameters, feature flags and integration end‑points, so new use cases can be launched quickly without heavy code changes.

Data Integration

• Securely connect platform to MES, ERP, PLM and eQMS data while enforcing Quasar’s data-governance policies.

Change Management

• Establish an AI Center of Excellence; design curricula for operators, engineers and executives.
• Document case studies showing cycle-time reduction, scrap reduction and audit-readiness gains.

Governance & Compliance

• Define guardrails for IP‑critical tasks and data; ensure zero deviations in internal and external audits.
• Establish an enterprise‑wide AI governance framework (policies, risk assessments, ethics reviews).

Qualifications

• Technical Depth:
• At least 10 years of professional experience and latest 5 years working in AI/ML specialisations
• Experience in Data Science, MLOps or Applied AI (Python, SQL, vector DBs, REST).
• Experience with LLM fine-tuning, prompt engineering, RAG architectures.
• Familiarity with cloud (AWS/GCP/Azure) and CI/CD for ML in regulated environments.
• Change management:
• Proven ability to introduce and implement new technology or processes across multiple functions, guiding teams through the full adoption lifecycle.
• Skilled at developing training materials, playbooks, and communication plans that make complex AI concepts accessible to non-technical stakeholders.
• Comfortable influencing without formal authority, engaging executives, managers, and frontline users to secure buy-in and sustain momentum.
• Education:
• BS/MS in Computer Science, Data/Industrial Engineering or related