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2 мСсяца Π½Π°Π·Π°Π΄

Machine Learning Engineer (Fintech)

Π€ΠΎΡ€ΠΌΠ°Ρ‚ Ρ€Π°Π±ΠΎΡ‚Ρ‹
onsite
Π’ΠΈΠΏ Ρ€Π°Π±ΠΎΡ‚Ρ‹
fulltime
Π“Ρ€Π΅ΠΉΠ΄
junior
Английский
b2
Π‘Ρ‚Ρ€Π°Π½Π°
Indonesia
Вакансия ΠΈΠ· списка Hirify.GlobalВакансия ΠΈΠ· Hirify Global, списка ΠΌΠ΅ΠΆΠ΄ΡƒΠ½Π°Ρ€ΠΎΠ΄Π½Ρ‹Ρ… tech-ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ
Для мэтча ΠΈ ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠ° Π½ΡƒΠΆΠ΅Π½ Plus

ΠœΡΡ‚Ρ‡ & Π‘ΠΎΠΏΡ€ΠΎΠ²ΠΎΠ΄

Для мэтча с этой вакансиСй Π½ΡƒΠΆΠ΅Π½ Plus

ОписаниС вакансии

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TL;DR
Machine Learning Engineer (Python/Go, AWS): Building scalable ML pipeline infrastructure for financial models in credit scoring, fraud, and risk with an accent on feature engineering, model deployment, and production reliability. Focus on transforming data science prototypes into maintainable systems, automating training and testing, and operating distributed data pipelines.

Location: Jakarta, Indonesia

Company

Building fast, affordable, and accessible financial services with a focus on user experience.

What you will do

  • Develop ML pipeline infrastructure for credit scoring, fraud, risk, and other financial models.
  • Build ETL processes that transform raw data into model features.
  • Create platforms to automate ML training, testing, and maintenance.
  • Partner with Data Scientists to turn predictive model prototypes into high-performance, integrated production systems.
  • Ensure data science code is maintainable, scalable, robust, and debuggable.

Requirements

  • 2–3 years of relevant Data or Software Engineering experience, including 1–2 years deploying ML models and feature pipelines.
  • Strong knowledge of data structures, data modeling, and software architecture for Machine Learning.
  • Production-ready Python and Go programming skills.
  • Strong experience with relational databases such as MySQL and PostgreSQL, plus NoSQL platforms such as S3.
  • Experience with ML libraries including TensorFlow/Keras, PyTorch, SparkML, or MLeap.
  • Experience with cloud services for data pipelines, monitoring, scheduling, and storage, preferably AWS; familiarity with Hadoop or Spark is required.

Culture & Benefits

  • Work on financial technology products designed to make financial services more accessible.
  • Collaborate closely with Data Scientists and cross-functional teams.
  • Build scalable and reliable Machine Learning and AI-related services.

Hiring process

  • Qualified candidates will be contacted by the Talent Acquisition team by phone or email.

Π‘ΡƒΠ΄ΡŒΡ‚Π΅ остороТны: Ссли Ρ€Π°Π±ΠΎΡ‚ΠΎΠ΄Π°Ρ‚Π΅Π»ΡŒ просит Π²ΠΎΠΉΡ‚ΠΈ Π² ΠΈΡ… систСму, ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡ iCloud/Google, ΠΏΡ€ΠΈΡΠ»Π°Ρ‚ΡŒ ΠΊΠΎΠ΄/ΠΏΠ°Ρ€ΠΎΠ»ΡŒ, Π·Π°ΠΏΡƒΡΡ‚ΠΈΡ‚ΡŒ ΠΊΠΎΠ΄/ПО, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡ‚Π΅ этого - это мошСнники. ΠžΠ±ΡΠ·Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎ ΠΆΠΌΠΈΡ‚Π΅ "ΠŸΠΎΠΆΠ°Π»ΠΎΠ²Π°Ρ‚ΡŒΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡˆΠΈΡ‚Π΅ Π² ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΡƒ. ΠŸΠΎΠ΄Ρ€ΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β†’