Principal AI Architect

5 days ago

Singapore HCL SINGAPORE PTE. LTD. Full time
Responsibilities
  • Design comprehensive AI solution and technology architecture, integrating latest AI technology developments into world-class solutions.
  • Lead high-level architectural discussions with clients, providing expert guidance on best practices for AI implementations across AI PC, Edge, Data Centre and Public Cloud environments.
  • Ensure solutions align with modern best practices across the full spectrum of platforms and environments.
  • Deep understanding across GPU/NPU, Cognitive Infrastructure, Application and Copilot/agent domains.
  • Contribute to HCL's thought leadership in the AI & Cloud domains with a deep understanding of opensource technologies (e.g., Kubernetes, OPEA) and partner technologies.
  • Collaborate on joint technical projects with global partners, including Google, Microsoft, AWS, NVIDIA, IBM, Red Hat, Intel, and Dell.
  • Architect innovative AI solutions from ideation to MVP, rapidly enabling genuine business value.
  • Optimize AI and cloud architectures to meet client requirements, balancing efficiency, accuracy and effectiveness.
  • Provide expert architectural and strategy guidance to clients on incorporating Generative AI into their business and technology landscape.

Requirements
  • Possess a Degree in Computer Science/Information Technology or related fields.
  • Minimum 10 years of architecture design and software engineering.
  • Professional-level expertise in Public Cloud environments (AWS, Azure, Google Cloud).
  • Expertise in AI model fine-tuning and evaluation, with a focus on improving performance for specialized tasks.
  • Demonstrable coding proficiency with Python, Java or Go languages.
  • Advanced machine learning algorithms, GenAI models (e.g., GPT, BERT, DALL-E, GEMINI), NLP techniques.
  • Working familiarity with Copilot solutions, in both software engineering and office productivity domains.
  • Knowledge of GenAI operations (LLMOps), experience Governing AI models in production environments.
  • Proficiency in data engineering for AI, including data preprocessing, feature engineering, and pipeline creation.

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