About the Role We are looking for a highly skilled and motivated AI ML Engineer with a strong background in LLMs, RAG pipelines, MLOps, and Azure Cloud . The ideal candidate should have deep experience in building and fine-tuning LLMs, deploying ML systems at scale, and integrating AI capabilities into enterprise-grade applications. Key Responsibilities Design and implement RAG (Retrieval-Augmented Generation) pipelines and AI agentic systems using LLM frameworks. Fine-tune LLMs and develop narrow, domain-specific models using industry best practices. Collaborate with data scientists and product teams to deploy ML models in production environments. Build and maintain robust MLOps pipelines using MLFlow , ensuring reproducibility and traceability of experiments. Integrate and deploy solutions using Azure DevOps and related cloud services. Implement secure and scalable authentication flows using Azure AD and GraphAPI . Optimize performance of deployed models with a focus on latency, cost, and maintainability. Document and present architecture, experiment results, and production workflows. Required Skills & Experience Strong foundational knowledge in Computer Science, IT systems, and architecture . Proficient in Python , with solid experience in both Object-Oriented and Functional Programming paradigms . Practical experience with LLMs , including open-source models. Hands-on experience with RAG pipelines and AI agentic frameworks . Expertise in MLOps tooling , especially MLFlow , model versioning, and CI/CD for ML. Solid understanding of model deployment and serving using Docker, Kubernetes, FastAPI, etc. Proven experience working with Azure cloud , including: Azure DevOps for pipelines and release management. Azure GraphAPI for organizational data access. Authentication and security protocols . Good communication skills and ability to work in cross-functional teams.