AI Automation Lead

1 day ago

Sydney, New South Wales, Australia beBeeStrategist Full time $150,000 - $190,000

AI Automation Strategist Role

Job Overview

The successful candidate will lead the development and implementation of AI and automation strategies across Marketplus and W Living portfolio.

This role involves translating business problems into structured AI solutions that reduce manual work, surface insights, accelerate revenue, and foster trust in our AI products among diverse stakeholders.

Main Responsibilities
  • Solution Development: Convert ambiguous business challenges into clear, actionable solutions leveraging appropriate AI models, APIs, and automation layers.
  • Cross-Functional Collaboration: Work closely with legal, marketing, and compliance teams to ensure their understanding and trust in our AI products and solutions.
  • Process Optimization: Continuously monitor and evaluate emerging AI trends, discerning what truly matters for our business versus what's merely shiny or nascent.
  • Project Implementation: Drive the AI roadmap, build scalable tool stacks, and lead the end-to-end implementation of AI projects.
Required Skills and Experience
  • Cloud Architecture Expertise: Strong understanding of AWS or Google Cloud architecture and integration, demonstrating experience in deploying and managing cloud-based solutions.
  • Comprehensive AI Knowledge: Comprehensive understanding of the AI landscape, including LLMs, agents, APIs, vector databases, automation tools, and no-code solutions.
  • Hands-on AI Experience: Hands-on experience building and deploying AI workflows or micro-products, whether in a startup or larger organizational setting.
  • Software Engineering Principles: Experience applying software engineering principles to data / AI engineering and analytics workflows.
  • Version Control and CI/CD: Experience with version control and CI/CD pipelines, specifically GitHub.
  • Data Science Tools: Practical hands-on experience with data science tools including Spark, Python, R, TensorFlow, PyTorch, and SQL.
  • ETL Processes and Data Pipelines: Strong understanding of ETL processes and data pipeline construction, including Cloud Composer and Airflow DAGs.
  • Docker Containerization: Familiarity with Docker for containerization and application deployment.

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