Machine Learning Operations Engineer

7 days ago


Stockholm, Stockholm, Sweden Truecaller Full time

About Truecaller

We are a Top 20 most downloaded apps globally, and world's #1 caller ID and spam-blocking service for Android and iOS, with extensive AI capabilities. Founded in 2009, we have an impressive year-on-year growth and high profitability, listed on Nasdaq OMX Stockholm, Large Cap.

Team Overview

Our team of 400 people from ~35 different nationalities is spread across our headquarters in Stockholm and offices in Bangalore, Mumbai, Gurgaon, and Tel Aviv with high ambitions. We collaborate across all of Truecaller, delivering products that require centralized ownership or where the competence to build the products is not present in the wider product organization.

Key Responsibilities

  • Lead the transformation of our MLOps platform by designing and building the infrastructure needed to enable fast, scalable, and cost-effective machine learning model development and deployment.
  • Help set the best ways of working to make the best use of the platform and increase developer productivity.
  • Collaborate closely and support developers throughout the organization to make model training, development, and monitoring fast, scalable, and cost-efficient.
  • Stay up to date with the latest trends and technologies in MLOps, AI, and cloud computing, and drive continuous innovation throughout Truecaller.

Requirements

  • 5+ years of experience in machine learning engineering, with a focus on deploying ML models in production environments.
  • Hands-on experience deploying ML models on mobile devices (e.g., TensorFlow Lite, Core ML, ONNX Runtime Mobile).
  • Strong understanding of machine learning frameworks (TensorFlow, PyTorch, ONNX) and their deployment on mobile platforms.
  • Experience building and maintaining tools and platforms for feature engineering, model training, model deployment, model monitoring, and A/B testing model performance.
  • Understanding of both Android and iOS development and how that impacts ML model deployment and usage.
  • Strong communication and collaboration skills, with the ability to work effectively across different teams and disciplines.

Bonus Requirements

  • Experience with AutoML tools (for example H2O.ai or Auto-Keras).
  • Experience enabling generative AI experimentation, fine-tuning, and deployment.


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