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3 дня назад

Junior Machine Learning Engineer (AI)

Π€ΠΎΡ€ΠΌΠ°Ρ‚ Ρ€Π°Π±ΠΎΡ‚Ρ‹
remote (Ρ‚ΠΎΠ»ΡŒΠΊΠΎ Latam)
Π’ΠΈΠΏ Ρ€Π°Π±ΠΎΡ‚Ρ‹
fulltime
Π“Ρ€Π΅ΠΉΠ΄
junior
Английский
b2
Π‘Ρ‚Ρ€Π°Π½Π°
Brazil
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

ВСкст:
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TL;DR

Junior Machine Learning Engineer (AI): Designing, developing, and deploying machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management. Focus on optimizing ML model inference performance to improve latency and cost-efficiency of AI applications.

Location: Must be based in LATAM

Company

hirify.global is a company in gaming industry.

What you will do

  • Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management.
  • Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real-time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run to support ML model training and inference.
  • Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation, leveraging tools like Vertex AI Model Monitoring and custom dashboards.
  • Optimize ML model inference performance to improve latency and cost-efficiency of AI applications.
  • Ensure the overall reliability, performance, and scalability of the ML models and data infrastructure platform.
  • Troubleshoot and resolve issues impacting ML models, data pipelines, and production AI systems.

Requirements

  • Junior ML Engineer, with a desire to develop and deploy machine learning models in production environments.
  • Understanding of Google Cloud Platform (GCP), including services relevant to ML and data infrastructure such as BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub and Composer (Airflow).
  • Knowledge containers (Docker) for deploying ML services.
  • Understanding of data pipelines and machine learning models in production environments.
  • Understanding of model monitoring, logging, and observability best practices for ML models and applications.
  • Knowledge of Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

Nice to have

  • Familiarity with AI orchestration concepts using tools like LangGraph or LangChain.
  • Experience includes working in gaming, real-time fraud detection, or AI personalization systems and Agentic workflows.

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†’

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