We’re looking for a motivated Junior Generative AI Developer to join our newly launched IT Pod project. This is a hands-on individual contributor role where you’ll collaborate with senior engineers to design, implement, and optimize cutting-edge Generative AI solutions. You’ll work with technologies like LLMs (GPT-4, Claude, Gemini), diffusion models, and multimodal systems — all while following ethical AI practices.1.Model Development & Fine-Tuning Assist in training and fine-tuning generative models (text, image, code) using PyTorch, TensorFlow, or JAX Implement RAG (Retrieval-Augmented Generation) pipelines and optimize prompts for specific domains 2. Tooling & Integration Build applications using LangChain, LlamaIndex, Hugging Face Transformers Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows 3. Prompt Engineering Design and test advanced prompting strategies (few-shot, chain-of-thought, ReAct) Create reusable prompt templates for workflows like customer support, code generation, and content moderation 4. Evaluation & Optimization Develop metrics for hallucination reduction, output consistency, and safety alignment Optimize inference costs using quantization, distillation, or speculative decoding 5. Collaboration Work with cross-functional teams (product, data, UX) to deploy AI solutions Document processes and contribute to knowledge-sharing sessions QualificationsEducation:Bachelor’s or Master’s in Computer Science, Data Science, or related fieldTechnical Skills: Proficiency in Python and familiarity with PyTorch or TensorFlow Basic understanding of NLP and neural architectures (Transformers, GANs) Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML) Familiarity with prompt engineering tools (LangChain, DSPy, Guidance, LMQL) Experience with deployment tools (FastAPI, Docker, MLflow) AI/GenAI Exposure:Experience with at least two of the following: Hands-on projects with LLMs or diffusion models Vector databases (Pinecone, Milvus) and orchestration tools Fine-tuning LLMs (Llama 2, Mistral) using LoRA, QLoRA, RLHF Building RAG pipelines with embedding models (BERT, OpenAI) Developing applications with Stable Diffusion, DALL·E NLP projects using spaCy or NLTK Soft Skills: Strong problem-solving mindset and curiosity about emerging AI trends Ability to explain technical concepts to non-technical stakeholders Preferred Qualifications Certifications:

Microsoft Certified: Azure AI Engineer Associate

Google Cloud Professional Machine Learning Engineer

What you can expect from us: Flexible work hours (we start between 7:00 and 10:30 am). 8-hour work time with a lunch break already included - spend the rest of the day doing what is important to you as intended in the #2h4Family program. Hybrid work model (50/50) Integration events - monthly delicious breakfasts, movie nights, board game nights, outdoor events. Lively and modern office in the City Centre with parking space for employees. A supportive and friendly atmosphere created by passionate people.