AI Engineer
New
O
OChKAI and cloud services
Miejsce pracy: Warszawa lub zdalnieFull-TimeMiddle
Salary14,000 - 17,000 PLN per month
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Job Details
- Languages
- Bardzo dobra znajomość języka polskiego i angielskiego (w mowie i piśmie); Languages: en B2
- Experience
- Min. 2 lata praktycznego doświadczenia w rolach: AI Engineer, MLOps Engineer lub Data Scientist, pracującego nad rozwiązaniami produkcyjnymi
- Required Skills
- DockerPythonGCPKubernetesMicrosoft AzurePyTorchPrompt EngineeringGenerative AILangChain
Requirements
- Have at least 2 years of practical experience as an AI Engineer, MLOps Engineer, or Data Scientist working on production solutions.
- Have practical knowledge of Generative AI, RAG architecture, prompt engineering, and fine-tuning.
- Have practical experience with frameworks such as LangChain, CrewAI, ADK, AutoGen, or Semantic Kernel.
- Have very good knowledge of Python and the standard ML stack: PyTorch, NumPy, Pandas, and Scikit-learn.
- Have practical experience with Google Cloud or Azure data and AI services.
- Know development practices and tools including Git, Docker, Kubernetes, CI/CD, and REST APIs.
- Have very good spoken and written Polish and English.
- Cloud AI/ML certifications are welcome, such as Google Cloud Professional ML Engineer or Microsoft Certified: Azure AI Engineer Associate.
- Experience with vector databases such as ChromaDB, Pinecone, Qdrant, or Pgvector is welcome.
- Experience in regulated sectors such as finance, banking, or healthcare is welcome.
Responsibilities
- Analyze customer needs in the context of ML/AI project implementation.
- Translate customer business requirements into AI solution architectures and support consulting and architecture teams.
- Design and support the creation, deployment, and scaling of applications based on GenAI, LLMs, RAG, and classical machine learning.
- Use native AI/ML services on Google Cloud, including Vertex AI, and Microsoft Azure, including Azure OpenAI and Azure AI Services.
- Build and automate data pipelines and processes for model training, evaluation, deployment, and production monitoring.
- Design, build, and automate evaluation processes for AI/ML applications.
- Support the organization's internal adoption of AI tools and solutions.
- Track AI trends, test new models and frameworks, and optimize deployments for cost and performance.
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