Empty studio scene: a round wooden table on the right lit by a softbox on the left, against a deep warm cove background.
Guru Balamurugan AI Engineer

AI that ships. Data that works.

AI prototypes are easy to impress with and hard to trust. I bring a data engineering foundation to AI engineering, building the retrieval, evaluation, and operational layers that turn promising demos into reliable production systems.

  1. Document extractor
  2. Podcast pipeline
  3. AI design team

Production risks I design around.

  1. 01

    Bad inputs, bad context

    Failure pattern

    The model gets incomplete, excessive, stale, or irrelevant context.

    Design response

    Clean ingestion metadata design retrieval testing context filtering

  2. 02

    Untrusted outputs

    Failure pattern

    Fluent answers can look correct before they are actually reliable.

    Design response

    Structured outputs schema validation confidence flags business rules

  3. 03

    No evaluation loop

    Failure pattern

    Without review, teams cannot measure drift, quality, or failure.

    Design response

    Eval sets human review approval states audit trails

  4. 04

    Silent failure

    Failure pattern

    Systems break quietly unless their behavior is observable.

    Design response

    Logging monitoring exception queues feedback loops