Chief AI Architect

1 week ago

Singapore beBeeGenerative Full time $150,000 - $200,000
AI Innovation Lead

We're seeking a highly skilled and experienced AI engineer to develop and implement cutting-edge AI solutions across cloud-native environments.

This role combines hands-on technical engineering with strategic leadership, enabling customers to translate AI innovation into scalable products from ideation to MVP and beyond.

  • Design, build, and deploy GenAI-driven applications using frameworks such as LangChain, OpenAI API, and Hugging Face Transformers.
  • Architect and implement scalable solutions within containerized, microservices-based cloud-native environments, including Kubernetes and OpenShift.
  • Integrate GenAI models and services seamlessly into existing platforms and services.
  • Collaborate with partner teams on advanced AI solution development.
  • Refactor and modernize legacy systems with 12-factor principles and cloud-native best practices.
  • Drive innovation in GenAI adoption through internal R&D, PoCs, and pilot programs.
  • Stay ahead of AI trends and apply the latest research into practical applications.
  • Provide expert consultation on GenAI architecture, deployment strategies, and business use case alignment.
  • Facilitate technical workshops, presentations, and strategic planning sessions with clients.
  • Serve as a trusted technical advisor on AI/ML integration, helping clients unlock value from AI technologies.
Requirements
  • Bachelor's degree in Computer Science, Information Technology, or a related field.
  • 5 years of software engineering experience, with strong programming skills in Java and Python.
  • Deep expertise in Generative AI frameworks, including LangChain, OpenAI, Hugging Face Transformers.
  • Strong foundation in Natural Language Processing (NLP): tokenization, embeddings, transformers, LLMs.
  • Experience with prompt engineering and optimizing AI model interactions.
  • Proven success designing and deploying cloud-native solutions using Kubernetes, Docker, and microservices.
  • Strong experience with Agile and DevOps practices, working in Jira-based environments.
  • Hands-on experience with MLOps (CI/CD for ML, model versioning, monitoring).
  • Proficiency in data engineering for AI: ETL, feature pipelines, data labeling, quality control.
  • Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate) and retrieval-augmented generation (RAG) techniques.
  • Cloud certifications from AWS, Azure, or Google Cloud Platform (GCP).

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