Lead AI Engineer (AI Architect)
Lead AI Engineer (AI Architect)
Job Details
Vacancies
1 position
Experience Required
No experience required
Job Description
We are looking for a Lead AI Engineer / AI Architect to join the Global Office based in Singapore.
he incumbent will be responsible for shaping, developing, and scaling AI solutions across the business. The position oversees the full lifecycle of AI platforms and services, ensuring they align with business goals and broader technology strategies.
Key Responsibilities
Strategic & Technical Leadership
- Define AI architecture and guide the design and implementation of AI platforms, solutions, and services.
- Provide expert leadership in machine learning, generative AI, and advanced analytics.
Solution Delivery & Execution
- Lead development and deployment of scalable AI applications to enhance operational and logistics efficiency.
- Conduct hands-on proof‑of‑concept work to validate new technologies.
Collaboration & Stakeholder Engagement
- Partner with business and technology stakeholders to integrate AI solutions effectively.
- Manage a portfolio of AI projects and ensure successful delivery.
Innovation & Knowledge Sharing
- Identify emerging technologies and methods, actively share expertise across teams and the wider organizational community.
Vendor & Technology Management
- Oversee vendor partnerships, evaluating AI offerings and ensuring alignment with business needs.
Requirements
- Advanced degree or equivalent experience in Computer Science, Engineering, or Information Systems.
- Minimum 10 years in data analytics, preferably with 5 years in solution architecture/MLOps, and 2 years in generative AI.
- Proven track record delivering impactful AI/ML solutions and integrating them into enterprise systems.
- Strong expertise in AI architecture, ML operations, model deployment, and cloud platforms (Azure, GCP).
- Hands-on experience with AI/LLM services and frameworks (e.g., Azure AI, OpenAI, Hugging Face).
- Proficient in Python, SQL, cloud-native architectures, distributed systems, and DevOps/MLOps tools.
- Familiarity with modern web technologies and containerization/orchestration tools (Docker, Airflow, MLFlow, Kubeflow).
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