AI Engineering Lead

2 weeks ago

Singapore AMSERS CONSULTING PTE. LTD. Full time $180,000 - $250,000 per year

About the Role

We are looking for an experienced AI Engineering Lead to drive the design, development, and deployment of enterprise-grade AI and GenAI solutions. This role will lead a small but growing engineering team, work closely with data scientists, product managers, and business stakeholders, and be responsible for scaling AI capabilities across the organization.

Key Responsibilities

  • Lead the end-to-end development and deployment of AI/GenAI solutions, from proof-of-concept to enterprise-scale implementation.
  • Build and manage a high-performing AI engineering team, providing technical leadership, mentorship, and best practices.
  • Collaborate with business units to identify AI/ML opportunities and translate them into scalable technical solutions.
  • Design system architecture to ensure solutions integrate seamlessly with existing enterprise IT and data infrastructure.
  • Oversee model lifecycle management, including training, deployment, monitoring, and retraining.
  • Drive adoption and change management by ensuring usability, performance, and business impact of AI solutions.
  • Stay updated with emerging AI/ML and GenAI trends, tools, and frameworks, and apply them where relevant.

Qualifications

  • Bachelor's or Master's degree in Computer Science, Engineering, or related field.
  • 7+ years of experience in software engineering, with at least 3+ years in AI/ML solution development.
  • Proven experience deploying AI/ML or GenAI solutions at enterprise scale (not just PoCs).
  • Strong hands-on experience with Python, TensorFlow, PyTorch, or other ML frameworks.
  • Experience with cloud platforms (AWS, GCP, Azure) and MLOps best practices.
  • Solid understanding of system design, APIs, and enterprise integration.
  • Strong stakeholder management and ability to work in complex, cross-functional environments.
  • Prior team leadership experience preferred.

Nice to Have

  • Experience in large-scale enterprise environments.
  • Exposure to GenAI applications (LLMs, multimodal models, prompt engineering).
  • Familiarity with data governance, security, and compliance in AI.

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