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Requirements:
- Professional Tenure: 8–10 years of experience as a Data Scientist or ML Engineer, with a proven track record of delivering production-grade AI solutions.
- Core Technical Stack: Mastery of Python and its scientific ecosystem (NumPy, Pandas,Scikit-learn, SciPy, Matplotlib).
- Advanced SQL & Big Data: Expert-level SQL skills with significant experience querying and manipulating petabyte-scale data in Google BigQuery.
- GCP Expertise: Deep hands-on experience with Vertex AI (AutoML, Pipelines, Model Registry, and Training) and Google Cloud Storage.
- Generative AI Mastery (Must-Have): * Practical experience building applications with LangChain or LlamaIndex.
- Proven expertise in RAG architectures, including vector embeddings and similarity search logic.
- Advanced Prompt Engineering skills for optimizing LLM outputs.
- Algorithmic Depth: Comprehensive understanding of supervised, unsupervised, and reinforcement learning algorithms, as well as the statistical foundations behind them.
- Deep Learning & Frameworks: Strong knowledge of Neural Networks and experience with TensorFlow or PyTorch for specialized deep learning tasks.
- Engineering & MLOps: Experience with MLflow or Kubeflow for experiment tracking and model deployment. Familiarity with Docker and Kubernetes for containerized ML workloads.
- API & Integration: Basic understanding of RESTful APIs and microservices to ensure models integrate seamlessly into broader software architectures.
- Software Best Practices: Proficiency in Git, unit testing for ML code, and reproducible research practices.
Key Skills
Ranked by relevance
ai
sql
neural networks
microservices
deep learning
restful apis
kubernetes
tensorflow
big data
kubeflow
pytorch
python
docker
mlflow
cloud
numpy
scipy
mlops
git
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- Posted
- Apr 08, 2026
- Type
- Contract
- Level
- Mid-Senior
- Location
- Poland
- Company
- iMedhas Consulting Services
Industries
IT Services
IT Consulting
Categories
Information Technology
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