Profile:
- At least 3 years of practical experience in machine learning engineering with a focus on generative AI and MLOps;
- Demonstrated expertise in building and deploying agentic workflows and agent tools;
- Familiarity with LLMs, prompt engineering, and agent frameworks such as LangChain, LlamaIndex or AutoGen;
- Strong proficiency in Python and ML frameworks including Scikit-Learn, SparkML, PyTorch and Hugging Face;
- Knowledge of distributed computing technologies such as PySpark and Hadoop;
- Solid understanding of MLOps principles including CI/CD, model monitoring, data versioning and automated retraining;
- Hands-on experience with MLOps platforms such as MLflow or Kubeflow;
- Proficiency in cloud platforms, with preference for Azure, and container technologies such as Docker and Kubernetes;
- Excellent problem-solving and communication skills;
- Structured and analytical approach to challenges.
Responsibilities:
- Design and d...