GenAI Engineer

About the Role We are looking for a passionate and hands-on Generative AI (GenAI) Engineer to join our team. You will work on designing, developing, and deploying AI-powered solutions leveraging Large Language Models (LLMs) and Azure OpenAI Services. This role requires both strong technical skills and creativity in applying AI to solve real-world business challenges. Key Responsibilities Design, develop, and deploy Generative AI applications using LLMs and Azure OpenAI Services. Implement prompt engineering, vector database integrations, and agentic AI workflows. Work with Azure AI Services including Azure OpenAI, Azure AI Search (Cognitive Search), Azure AI Document Intelligence, Azure AI Content Safety, Azure Cognitive Services (Vision, Language, Speech), Azure Machine Learning, and Azure Blob Storage for end-to-end AI solution development. Design and integrate Model Context Protocol (MCP) servers to extend LLM capabilities with external tools, APIs, and enterprise data sources; build custom MCP servers using Python SDKs for agentic workflows. Develop scalable data processing and model-serving pipelines in Python. Write efficient SQL queries for data extraction, transformation, and analysis. Manage code repositories using Git and containerize applications with Docker. Optimize AI workflows for performance, scalability, and cost-efficiency. Collaborate with cross-functional teams including data engineers, software developers, and product managers. Required Skills Qualifications 1-3 years of experience in data science, AI/ML development, or related roles. Min. 1 year Hands-on experience with Generative AI and LLMs (e.g., GPT, Azure OpenAI, Hugging Face). Knowledge of prompt engineering techniques, agentic AI frameworks (e.g. CrewAI, MS AutoGen, LangGraph, LangChain), and inter-agent protocols including Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication patterns. Knowledge of retrieval Augmented Generation (RAG) architecture. Practical understanding of Model Context Protocol (MCP): building or consuming MCP servers to connect LLMs with tools, databases, and APIs; familiarity with MCP server lifecycle, resource exposure, and tool definitions. Experience with Vector Databases (e.g., Azure AI Search, Milvus, Pinecone, Weaviate, ChromaDB) for building semantic search and RAG pipelines. Hands-on experience with Azure AI Services: Azure OpenAI (deployments, fine-tuning, embeddings), Azure AI Search with semantic ranking, Azure AI Content Safety for guardrails, Azure AI Document Intelligence for structured data extraction, and Azure Blob Storage for data ingestion pipelines. Strong programming skills in Python and SQL. Experience with Git, Docker, and working in Linux environments. Understanding of safety guardrails and responsible AI practices for LLM applications, including content filtering, prompt injection defense, output validation, and use of Azure AI Content Safety or similar tools. Good to Have Knowledge of Sinequa tool for developing GenAI applications. Experience with CI/CD pipelines for ML/AI deployments. Exposure to LLMOps best practices: prompt versioning, experiment tracking (MLflow, Azure ML), model evaluation frameworks (RAGAS, TruLens), and AI observability tools (Langfuse, Arize, Promptflow). Familiarity with Azure cloud-native AI architectures including Azure Functions, Azure Container Apps, Azure API Management for AI gateways, and Azure Monitor / Application Insights for GenAI workload observability. Experience building or consuming MCP servers for enterprise tool integration (e.g., connecting LLMs to internal databases, ERP/CRM systems, or IoT data streams via MCP). Awareness of emerging AI trends: multimodal LLMs (Azure AI Vision + GPT-4o), small language models (SLMs) for edge/on-premise inference (Phi-3/Phi-4), and AI agent orchestration patterns. Soft Skills Problem-solving mindset with a passion for learning new AI technologies. Strong communication skills to explain technical concepts to non-technical stakeholders. Ability to work in a fast-paced, collaborative, and innovative environment. Team player.