Senior Machine Learning Engineer
New
Fully remote job; Locations: Wrocław, Fabryczna 6, Wrocław, Country code: PLContractSenior
Salary not disclosed
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Job Details
- Languages
- En B2
- Experience
- 5+ years
- Required Skills
- PythonSQLMLFlowPyTorchTensorflowscikit-learnNLPLLMMLOps
Requirements
- 5+ years of experience in Machine Learning Engineering with production-grade AI systems.
- Expert-level Python skills and strong knowledge of ML libraries such as PyTorch, TensorFlow, scikit-learn, Pandas, and NumPy.
- Hands-on experience with transformer architectures and LLMs (e.g. GPT, BERT, Llama).
- Experience building NLP and Generative AI solutions using frameworks such as Hugging Face and LangChain.
- Practical experience implementing RAG (Retrieval-Augmented Generation) architectures and semantic search systems.
- Experience with prompt engineering techniques and LLM optimization strategies.
- Strong understanding of predictive modeling, anomaly detection, and forecasting techniques.
- Experience deploying and maintaining ML systems in production environments.
- Practical knowledge of MLOps practices, including Docker, Kubernetes, MLflow, and automated ML pipelines.
- Experience designing data preprocessing pipelines for structured and unstructured data sources.
- Strong SQL and data processing skills.
- Solid understanding of model performance optimization, monitoring, and retraining strategies.
- Strong communication and collaboration skills.
Responsibilities
- Design and develop production-grade Machine Learning and Generative AI solutions.
- Build and optimize NLP and LLM pipelines for document processing and requirements extraction.
- Develop RAG (Retrieval-Augmented Generation) systems and semantic search solutions.
- Create predictive models for forecasting, anomaly detection, and risk scoring.
- Implement prompt engineering strategies to improve LLM performance on domain-specific tasks.
- Design and maintain automated ML pipelines and model deployment workflows.
- Deploy and monitor ML models in secure production environments.
- Optimize model inference performance and scalability.
- Build preprocessing pipelines for both structured and unstructured data sources.
- Collaborate with Data Engineers, Data Scientists, Backend Engineers, and domain experts to deliver end-to-end AI solutions.
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