Despre poziție
Responsibilities:
• Manage the end-to-end lifecycle of machine learning models, including training, deployment, monitoring, and ongoing improvements.
• Design and implement robust ML pipelines covering data ingestion, feature generation, model training, and retraining processes.
• Build and maintain high-performance inference services capable of serving models in production environments.
• Continuously optimize model performance and infrastructure efficiency, focusing on latency, scalability, and cost optimization.
• Collaborate with DevOps and infrastructure teams to ensure reliable deployment, system scalability, and operational stability.
• Implement best practices around model versioning, CI/CD pipelines, and reproducible experiments.
• Monitor deployed models and datasets to detect performance degradation, data drift, or anomalies, and implement improvements when needed.
• Work closely with technical and business stakeholders to translate machine learning outcomes into tangible product or operational improvements.
• Contribute to improving engineering standards, architecture decisions, and ML best practices within the team.
Cerințe
We are looking for an experienced Machine Learning Engineer who can take ownership of bringing machine learning systems into production and ensuring they run reliably at scale. This role focuses on building, deploying, and continuously improving ML solutions that operate in high-impact, production environments.
The ideal candidate has a strong mix of machine learning expertise, software engineering discipline, and MLOps experience, and is comfortable working independently while collaborating with engineering and infrastructure teams to improve platform reliability and performance.
This role requires someone who has already worked on real-world ML deployments, understands the challenges of operating models in production, and can drive improvements across the entire ML lifecycle - from training and deployment to monitoring and optimization.
Key requirements:
Master’s degree, PhD, or equivalent practical experience in Computer Science, Engineering, Data Science, or a related field.
At least 6 years of experience working with machine learning systems in production environments.
Strong Python programming skills and solid software engineering practices.
Proven experience deploying ML models into production systems and maintaining them at scale.
Hands-on experience with machine learning frameworks such as PyTorch or scikit-learn.
Experience building and maintaining ML pipelines and reproducible workflows.
Strong understanding of MLOps principles, including model lifecycle management and deployment strategies.
Experience working with containerized environments and DevOps practices.
Knowledge of model performance optimization techniques, including latency reduction, batching strategies, quantization, and GPU/CPU optimization.
Familiarity with monitoring and reliability practices for ML systems, including metrics, logging, alerting, and model performance tracking.
Tech stack:
Candidates are expected to have experience with technologies such as:
Python
Kubernetes
Cloud platforms (AWS or Azure)
Model optimization tools and techniques (e.g., ONNX, TensorRT, quantization)
Containerization and CI/CD pipelines
Monitoring and observability tools for production systems
Experience with tools or platforms such as FastAPI, SageMaker, feature stores, model registries, or service meshes is considered an advantage.
Despre Companie
Our client is an international technology company that develops advanced content moderation solutions used by global online platforms. Their product combines cutting-edge artificial intelligence technologies such as machine learning, natural language processing, and computer vision with human moderation workflows to help organizations maintain safe and trusted digital communities.
The company operates globally with distributed engineering teams and focuses on building scalable SaaS platforms capable of processing large volumes of data in real time. Their solutions support some of the world’s largest online marketplaces, social platforms, and digital communities by detecting and managing harmful or inappropriate content.
The engineering teams work on high-performance systems, AI-driven automation, and modern cloud-based infrastructures, continuously improving the reliability, scalability, and efficiency of their moderation technologies.