AI/ ML /Llm Engineer

We are building a talent pool of experienced AI / ML / LLM Engineers who would like to collaborate with Kyotu Technology on upcoming projects.

Our work focuses on applied AI, combining machine learning, optimization, and modern LLM-based systems. We work on a variety of use cases across domains such as automation, data processing, intelligent systems, and AI-driven product development.

We are especially interested in engineers with experience or strong interest in agentic AI systems, who enjoy working at the intersection of classical ML and modern generative AI.

You will have the opportunity to contribute to projects involving forecasting, optimization, reinforcement learning, and LLM-powered workflows, including autonomous or semi-autonomous agents.

🚀 Must haves

• minimum 4 years of experience in AI / ML engineering
• strong Python skills and experience with data/ML libraries (e.g. NumPy, Pandas)
• experience in building and evaluating machine learning models
• experience with LLMs and GenAI use cases (e.g. prompt engineering, RAG, LLM pipelines)
• experience or strong interest in agentic AI systems (multi-step workflows, tool usage, autonomous agents)
• experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
• understanding of data processing, feature engineering, and model evaluation
• experience with experimentation tools (e.g. MLflow, Weights & Biases)
• ability to work across research and production environments
• high level of independence, curiosity, and problem-solving mindset
• good communication skills and team orientation
• English at B2 level

✨ Nice to have

• experience with reinforcement learning (e.g. PPO, SAC, TD3)
• experience with optimization tools (e.g. PuLP, Pyomo)
• experience with advanced forecasting methods (e.g. Prophet, LightGBM, Transformers)
• familiarity with LLM orchestration frameworks (e.g. LangChain, LlamaIndex)
• experience with vector databases (e.g. Pinecone, Weaviate, FAISS)
• experience with cloud platforms (e.g. AWS, Azure, GCP)
• experience with MLOps practices and CI/CD for ML