Ml Model Developer - As an ML Engineer on our team, you will design, train, fine-tune, and deploy ML/LLM models that power autonomous exception resolution, anomaly detection, and explainable insights. Design, train, fine-tune, and deploy ML/LLM models for production. Build RAG pipelines using vector databases and frameworks such as LangChain, LangGraph, and MCP. Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions. Collaborate with data engineering to maintain data pipelines for ML workloads. Conduct feature engineering and embeddings generation on structured and unstructured data. Implement model monitoring, drift detection, and retraining pipelines. Explore emerging LLM/SLM architectures and multi-agent orchestration patterns. Collaborate cross-functionally with R&D, data science, product, and engineering teams. Mentor junior engineers and contribute to best practices in ML engineering. Requirements Bachelor''s or Master''s degree in Computer Science, Data Science, Machine Learning, or related field. 3 years building and deploying ML systems. English advance (B2) Strong Python skills and experience with PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers. Hands-on experience with LLMs/SLMs including fine-tuning, prompt design, and inference optimization. Familiarity with at least two of the following: OpenAI GPT, Anthropic Claude, Google Gemini, Meta LLaMA. Knowledge of vector databases, embeddings, and RAG pipelines. Experience working with both structured and unstructured data at scale. Understanding of SQL and distributed data frameworks like Spark or Ray. Deep knowledge of ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.