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You’ll have the opportunity to design and implement models from the ground up, experiment with the latest deep learning and LLM technologies, and deploy intelligent systems running on modern cloud infrastructure.\n\n Responsibilities:\n\n Design and develop data-driven models across a wide range of tasks, from traditional classification and segmentation to advanced neural networks and deep learning.\n\nWork with LLMs and agentic AI systems, integrating them into larger workflows and applications.\n\nConduct exploratory data analysis, feature engineering, and model evaluation.\n\nArchitect and deploy scalable ML pipelines using AWS services such as Lambda, ECS, Step Functions, SQS, S3, and related cloud tools.\n\nPrepare and manage datasets using Python and core data libraries.\n\nImplement model training, fine-tuning, and optimization routines.\n\nBuild and serve models in production environments — from REST APIs to event-driven or batch pipelines.\n\nCollaborate with cross-functional teams to design practical AI solutions that deliver real business value.\n\nContinuously monitor, evaluate, and improve model performance and system scalability.\n\n Candidate Requirements:\n\n Strong proficiency in Python and core data science libraries (NumPy, pandas, scikit-learn, TensorFlow, Keras, PyTorch, etc.).\n\nSolid understanding of machine learning fundamentals, including supervised/unsupervised learning, feature extraction, model validation, and optimization.\n\nExperience with neural networks, deep learning architectures, and modern LLMs (e.g., GPT, Gemma, Claude, Mistral).\n\nHands-on experience with AWS infrastructure — especially Lambda, ECS, Step Functions, SQS, and S3 — or equivalent cloud platforms (Azure, GCP).\n\nFamiliarity with data storage systems, including both relational (SQL) and NoSQL databases.\n\nStrong analytical thinking and problem-solving skills with attention to scalability and maintainability.\n\nAbility to build full ML workflows — from data ingestion to model deployment — autonomously.\n\nGood communication and documentation skills for presenting results and design choices clearly.\n\n Preferred / Nice to Have:\n\n Experience with Docker, API-based model serving, or microservice architectures.\n\nKnowledge of vector databases, embedding models, and semantic search.\n\nCuriosity and creativity in experimenting with Agentic AI systems and automation workflows.\n\nInterest in continuous learning and staying current with the latest AI research and tools.\n\nJoin us if you want to work on cutting-edge data science challenges, shape next-generation AI applications, and have the freedom to bring your own ideas from prototype to production.\n\n Employee perks:\n\n Work life balance\n Flexible work hours\n Hybrid work model\n\n Health\n Health insurance\n Gym subscription\n Influenza vaccinations\n Opportunities to take part in hikes, runs, padel games with teammates\n\n Office\n Table football\n Ping pong\n Free snacks\n Bicycle 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