Informacje o stanowisku

Technical Expertise

• Generative AI & LLMs: Experience with open-source (DeepSeek, Qwen, Bielik) and commercial models (on-prem/cloud). Understanding of Transformer, Encoder-Decoder, and Mixture of Experts (MoE) architectures. Practical knowledge of RAG (Retrieval-Augmented Generation).
• Fine-Tuning: Adapting LLMs to domain-specific data; dataset curation; hyperparameter optimization; experience with PEFT, LoRA; model evaluation and drift monitoring.
• Vector Databases: Hands-on experience with Qdrant, Pinecone, Weaviate, Milvus; integration with OpenSearch/ElasticSearch.
• AI Frameworks & MLOps: Proficiency with LangChain, LlamaIndex, Spring AI, MLflow, Kubeflow, Azure ML, AWS Sagemaker; deploying models with Docker/Kubernetes.
• Cloud & Integrations: Experience with Azure AI (Cognitive Search, OpenAI Service), AWS Bedrock/Kendra, GCP Vertex AI; integrating AI with enterprise apps (.NET, Java, Python, REST/GraphQL APIs).

Business Experience

• Understanding of business processes in manufacturing, healthcare, energy, and finance.
• Translating business challenges into AI use cases (e.g. automation, paperless workflows, predictive maintenance).
• Supporting Go-To-Market: proposals, PoCs, and client demos.
• Technical pre-sales and stakeholder engagement.

,[Promote and scale GenAI usage in software engineering processes., Deploy AI tools supporting developers — code generation, test automation, documentation, and refactoring., Accelerate and reduce PoC and estimation costs using AI (e.g. auto-generating backlogs, user stories, and architectures)., Build internal GenAI use cases — developer assistants, documentation generators, error analyzers., Mentor teams and deliver internal training on AI practices., Track GenAI trends, assess innovations, and recommend enterprise-grade applications., Support GTM and Pre-Sales teams in creating AI-powered demos, offers, and solution concepts. Requirements: Python, AI, Cloud, Elasticsearch, Spring, MLflow, Kubeflow, Azure, AWS, Docker, Kubernetes, GCP, .NET, Java, REST API, GraphQL, Use cases Additionally: Sport subscription, Training budget, Private healthcare, Flat structure, International projects, Free coffee, Bike parking, Canteen, Playroom, Free snacks, Free beverages, Mobile phone, In-house trainings, In-house hack days, Modern office, Startup atmosphere, No dress code.

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