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Machine Learning / Data Engineer

Machine Learning / Data Engineer

Success Pact Consulting Pvt LtdBangalore
30+ days ago
Job description

Job Title : ML (Data) Engineer

Location : Bangalore

Experience : 510 Years

Role Overview :

We are looking for a skilled ML (Data) Engineer to design, build, and scale data management systems that power our AutoML and forecasting platforms. The ideal candidate will be responsible for managing feature stores, data pipelines, and time series workflows, ensuring high reliability, scalability, and usability for ML teams.

Key Responsibilities :

  • Build and scale data management systems to support AutoML and forecasting pipelines.
  • Own and evolve the feature store and feature engineering workflows.
  • Implement robust data SLAs and lineage systems across time series data pipelines.
  • Collaborate with ML engineers, infrastructure, and product teams to design scalable and user-aware platform solutions.
  • Drive architectural decisions around data distribution, versioning, and composability.
  • Participate in the design of reusable systems for varied supply chain and forecasting problems.

Requirements :

  • Strong experience working with large-scale data systems (Big Data, distributed pipelines).
  • Hands-on experience with ETL pipelines, data lineage, and data reliability tooling.
  • Proven experience in ML feature engineering and / or building feature stores.
  • Exposure to time series data, forecasting pipelines, or AutoML workflows.
  • Strong problem-solving and design-thinking abilities, capable of breaking down ambiguous platform problems.
  • Good-to-Have :

  • Familiarity with modern data infrastructure (e.g., Apache Iceberg, ClickHouse, Data Lakes).
  • Strong product thinking to anticipate user interaction with the system.
  • Experience building composable, user-extensible systems.
  • Prior exposure to AutoML frameworks (e.g., SageMaker, VertexAI) or internal ML platforms.
  • Ideal Candidate :

    A data-focused ML engineer with hands-on experience in feature engineering, large-scale pipelines, and AutoML platforms, capable of designing reliable, scalable, and user-friendly systems for ML applications.

    (ref : hirist.tech)

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