PEOPLE HUNT | iGaming | IT | FinTech | Crypto
1 - 10 employees
🎯 Recruiter
đź’ł Fintech
🎮 Gaming
Recruitment • Fintech • Gaming
PEOPLE HUNT is a global recruitment partner specializing in connecting top talent with innovative companies primarily in the iGaming, IT, and FinTech sectors. With over 15 years of expertise, PEOPLE HUNT understands the unique hiring challenges within these industries and offers tailored hiring solutions. They foster a collaborative work environment focusing on personal growth, flexibility, and a supportive team culture, ensuring that both employees and clients thrive on their success.
ML Engineer
Job not on LinkedIn
đź•’ March 20
🗣️🇷🇺 Russian Required
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PEOPLE HUNT | iGaming | IT | FinTech | Crypto
1 - 10 employees
🎯 Recruiter
đź’ł Fintech
🎮 Gaming
Recruitment • Fintech • Gaming
PEOPLE HUNT is a global recruitment partner specializing in connecting top talent with innovative companies primarily in the iGaming, IT, and FinTech sectors. With over 15 years of expertise, PEOPLE HUNT understands the unique hiring challenges within these industries and offers tailored hiring solutions. They foster a collaborative work environment focusing on personal growth, flexibility, and a supportive team culture, ensuring that both employees and clients thrive on their success.
đź“‹ Description
• Maintain and evolve an existing recommendation system in a multi-tenant environment • Bring models from experimentation into production and improve their real-world performance • Build and support end-to-end ML pipelines : training, validation, deployment, retraining • Optimize training/inference workloads for latency, reliability, memory usage, and scalability • Integrate ML inference into Python-based backend services and collaborate with backend/data teams • Define and track model KPIs linked to product and business impact (engagement/retention/revenue) • Improve data quality , feature availability, and feature pipelines (Airflow-based workflows) • Set up monitoring for model health: drift, degradation, anomalies, incident response • Run controlled experiments (A/B tests), analyze results, and translate insights into improvements • Document model behavior, assumptions, and operational runbooks • Contribute to architecture decisions for scalable ML infrastructure and deployment practices
🎯 Requirements
• 5+ years in ML Engineering / Production ML roles • Degree in a quantitative field (Math/Stats/CS or similar) • Strong Python skills and experience building production-grade ML services • Solid ML foundation: supervised learning, ranking/recommendations, evaluation methodology • Hands-on experience with feature engineering for event/behavioral data • Production deployment experience: APIs and/or batch jobs, Airflow, CI/CD, containers • Practical SQL knowledge and understanding of data access patterns • Experience with monitoring ML systems end-to-end: data quality + model performance + alerts • Understanding of experimentation and statistics (A/B testing, experiment design) • Strong engineering habits: testing, code review, documentation • Computer science fundamentals (processes, memory, performance considerations)
🏖️ Benefits
• wellness program • medical compensation • sports support • paid sick leaves • 21 vacation days + personal days • learning budget • English club • equipment provided • workplace setup bonus • team events
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