255,160 PLN per annum, plus company benefits Overview: Williams Lea seeks a senior ML engineer to design, develop, deploy, and maintain scalable machine learning and Generative AI solutions for enterprise and client-facing applications. The role involves end-to-end ML pipelines, LLM integrations, cloud-native AI platforms, and production-grade AI systems across AWS and Azure environments. The candidate should have strong Python development skills, ML model deployment experience, cloud technologies, and MLOps practices. Key responsibilities:

• Design, build, and deploy scalable ML and Generative AI solutions in production environments.
• Develop end-to-end ML pipelines (data preprocessing, feature engineering, model training, evaluation, deployment, monitoring).
• Integrate LLMs and AI services into enterprise applications and workflows.
• Work with cloud-native AI services including AWS SageMaker, Bedrock, Lambda, S3, CloudWatch, and Azure ML.
• Develop and optimize ML models using Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or Hugging Face frameworks.
• Implement MLOps best practices: CI/CD pipelines, model versioning, automated testing, monitoring, governance.
• Collaborate with Data Scientists, Platform Engineers, Backend Teams, Product Owners, and Solution Architects.
• Support production releases, troubleshooting, model monitoring, and performance optimization.
• Ensure compliance with security, governance, and responsible AI standards.
• Contribute to AI architectu

255,160 PLN per annum, plus company benefits Overview: Williams Lea seeks a senior ML engineer to design, develop, deploy, and maintain scalable machine learning and Generative AI solutions for enterprise and client-facing applications. The role involves end-to-end ML pipelines, LLM integrations, cloud-native AI platforms, and production-grade AI systems across AWS and Azure environments. The candidate should have strong Python development skills, ML model deployment experience, cloud technologies, and MLOps practices. Key responsibilities:

• Design, build, and deploy scalable ML and Generative AI solutions in production environments.
• Develop end-to-end ML pipelines (data preprocessing, feature engineering, model training, evaluation, deployment, monitoring).
• Integrate LLMs and AI services into enterprise applications and workflows.
• Work with cloud-native AI services including AWS SageMaker, Bedrock, Lambda, S3, CloudWatch, and Azure ML.
• Develop and optimize ML models using Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or Hugging Face frameworks.
• Implement MLOps best practices: CI/CD pipelines, model versioning, automated testing, monitoring, governance.
• Collaborate with Data Scientists, Platform Engineers, Backend Teams, Product Owners, and Solution Architects.
• Support production releases, troubleshooting, model monitoring, and performance optimization.
• Ensure compliance with security, governance, and responsible AI standards.
• Contribute to AI architectu