Senior Engineer GENAI Azure

• Design, architect, and implement end-to-end Generative AI solutions using Azure OpenAI, LLMs, and RAG frameworks.
• Build and optimize AI/ML pipelines, including data ingestion, preprocessing, embedding generation, and vector indexing.
• Develop and maintain Azure-based AI infrastructure, including Azure OpenAI Service, Cognitive Search, Azure ML, Kubernetes, and related cloud services.
• Implement RAG-based conversational and knowledge retrieval systems for enterprise use cases.
• Integrate LLMs with existing business applications, APIs, and enterprise systems.
• Work with cross-functional teams—including product, data engineering, architecture, and business stakeholders—to define and deliver AI-driven solutions.
• Lead the evaluation of new AI tools, frameworks, and methodologies to drive innovation and scalability.
• Ensure solutions adhere to enterprise security, governance, privacy, and compliance standards.

Required Skills & Experience

• 13+ years of overall software engineering experience with strong coding background in Python, C#, or JavaScript/TypeScript.
• Hands-on experience with Generative AI and LLMs (GPT, Llama, Claude, or similar).
• Strong expertise in building RAG architectures, vector databases (Pinecone, FAISS, Chroma, Azure Cognitive Search), embeddings, and prompt engineering.
• Deep knowledge of Azure Cloud Services, including:
  • Azure OpenAI
  • Azure Cognitive Search
  • Azure ML
  • Azure Kubernetes Service (AKS)
  • Azure Functions, Logic Apps, App Services
• Proven experience developing and deploying production-grade AI/ML applications.
• Strong understanding of MLOps, CI/CD, versioning, model lifecycle management.
• Excellent communication and stakeholder management skills—ability to translate business needs into actionable technical solutions.
• Experience working in Agile/Scrum environments.

Preferred Qualifications

• Prior experience building enterprise AI chatbots, copilots, or AI agent workflows.
• Experience with LangChain, LlamaIndex, Semantic Kernel, or similar orchestration frameworks.
• Familiarity with data governance, security, model safety, and responsible AI best practices.
• Experience collaborating with architecture and security teams to define scalable AI patterns.