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

About the role

Senior ML Engineer Employment: Full Time Work model: Hybrid

Salary

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

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