Your role & responsibilities As a Machine Learning Engineer, you will: Design, develop, and deploy ML and NLP models to solve business problems in areas like customer service, compliance, fraud, and operational efficiency. Build and optimize retrieval-augmented generation (RAG) pipelines integrating LLMs and knowledge bases. Develop and enhance chatbots and virtual assistants using GenAI and conversational AI frameworks. Collaborate with cross-functional teams to deliver AI products on Microsoft Azure cloud infrastructure. Automate end-to-end ML workflows using tools like Azure ML, Databricks, and MLOps pipelines. Ensure models are secure, ethical, explainable, and compliant with financial industry regulations. Contribute to continuous learning, experimentation, and evaluation of new GenAI tools, open-source frameworks, and model architectures. Key qualifications Must-haves 3 years of experience in Machine Learning, with a focus on NLP or AI applications Strong Python programming skills (e.G., scikit-learn, PyTorch, Transformers, Langchain) Solid experience working with Azure cloud services (Azure ML, Azure Functions, Azure DevOps, etc.) Hands-on experience with LLMs, RAG frameworks, vector databases, and embedding models Background in building or scaling chatbots, Q&A systems, or conversational AI Familiarity with data engineering principles and MLOps practices Good understanding of model evaluation, bias detection, and explainability in ML systems Nice-to-have: Experience with tools like Openai API, Hugging Face, Langchain, Weaviate, Pinecone, or Qdrant Knowledge of regulatory or compliance frameworks in banking (e.G., GDPR, data privacy) Previous experience in the financial services or banking industry Soft skills: Strong analytical and problem-solving skills Clear and effective communicator, both with technical teams and business stakeholders Passion for experimentation and emerging AI trends A team player with a proactive and solution-driven mindset