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AI Engineer

Join a confidential innovation program to build and evolve a semantic matching engine that turns complex data into precise, explainable recommendations. You will combine cloud AI services, robust pipelines, and operational excellence to make the platform smarter every day.

CONTEXT

A confidential innovation program is building a next-generation matching engine that must turn complex, fragmented information into fast and explainable recommendations. The platform already has a strong foundation, but the next chapter is about scaling intelligence: sharper semantic search, better fuzzy matching, cleaner data pipelines, and a cloud environment that can be operated with confidence. The team is looking for an AI engineer who enjoys connecting experimentation with production reality and who can make the engine smarter without making it fragile. The role sits at the intersection of data science, platform engineering, and operational excellence, with a clear mandate to transfer knowledge and keep the solution maintainable long after the first deployment.

ACTIVITIES

  • Design and evolve data pipelines that feed the matching engine with reliable, well-understood data.
  • Implement semantic search and fuzzy matching features using cloud AI services and search technologies.
  • Deploy and configure Microsoft environments, including Azure AI Foundry, Azure AI Search, and Microsoft Fabric.
  • Build CI/CD workflows that make releases repeatable, observable, and safe.
  • Monitor data quality, investigate anomalies, and improve match relevance over time.
  • Work closely with product, engineering, and operations stakeholders to turn feedback into technical improvements.
  • Document the architecture and transfer knowledge so that internal teams can operate the platform confidently.

EXPECTED ACHIEVEMENTS

  • A more accurate and explainable matching engine that handles real-world ambiguity gracefully.
  • A reliable cloud deployment model with automated checks, observability, and maintainable pipelines.
  • Measurable improvements in data quality and match performance.
  • A strong operational handover that reduces dependency on individual experts and increases team autonomy.
  • A platform that can evolve quickly without compromising security, stability, or trust.

ENVIRONMENT

  • Microsoft Azure, Azure AI Foundry, Azure AI Search, Microsoft Fabric.
  • Python-based data and AI workflows, CI/CD tooling, and cloud deployment practices.
  • Semantic search, fuzzy matching, data quality monitoring, and operational maintenance.
  • A collaborative delivery culture with a strong emphasis on knowledge transfer and documentation.