MLOps Engineer

Appnext offers end-to-end discovery solutions covering all the touchpoints users have with their devices. Thanks to Appnext’s direct partnerships with top OEM brands and carriers, user engagement is achieved from the moment they personalize their device for the first time and throughout their daily mobile journey.

Appnext ‘Timeline’, a patented behavioral analytics technology, is uniquely capable of predicting the apps users are likely to need next. This innovative solution means app developers and marketers can seamlessly engage with users directly on their smartphones through personalized, contextual recommendations.

Established in 2012 and now with 12 offices globally, Appnext is the fastest-growing and largest independent mobile discovery platform in emerging markets.

As a Machine Learning Engineer , you will be in charge of building end-to-end machine learning pipelines that operate at a huge scale, from data investigation, ingestions and model training to deployment, monitoring, and continuous optimization. You will ensure that each pipeline delivers measurable impact through experimentation, high-throughput inference, and seamless integration with business-critical systems. This job combines 70% machine learning engineering and 30% algorithm engineering and data science.

We're seeking an Adtech pro who thrives in a team environment, possesses exceptional communication and analytical skills, and can navigate high-pressure demands of delivering results, taking ownership, and leveraging sales opportunities.

Responsibilities :

• Build ML pipelines that train on real big data and perform on a massive scale.
• Handle a massive responsibility, Advertise on lucrative placement (Samsung appstore, Xiaomi phones, TrueCaller). Train models that will make billions of daily predictions and affect hundreds of millions users.
• Optimize and discover the best solution algorithm to data problems, from implementing exotic losses to efficient grid search.
• Validate and test everything. Every step should be measured and chosen via AB testing.
• Use of observability tools. Own your experiments and your pipelines.
• Be Frugal. Optimize the business solution at minimal cost.
• Advocate for AI. Be the voice of data science and machine learning, answering business needs.
• Build future products involving agentic AI and data science.
• Affect millions of users every instant and handle massive scale

Requirements :

Preferred :