As a Senior Generative AI Engineer, you will lead the design, development, and deployment of production-grade AI solutions. You aren't just building prototypes; you are architecting scalable RAG systems, fine-tuning models for specific domain expertise, and ensuring that GenAI capabilities integrate seamlessly into enterprise-level applications.

Key Responsibilities :

- LLM Orchestration: Design and deploy sophisticated LLM-based applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel.

- Model Optimization: Lead the fine-tuning of open-source and proprietary models to improve performance, latency, and cost-efficiency.

- Advanced RAG Systems: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines utilizing vector databases like Pinecone, FAISS, Weaviate, or Azure AI Search.

- Scalable Deployment: Containerize services using Docker and deploy via FastAPI and Azure Functions to ensure high availability and low latency.

- Productization: Build and scale enterprise-grade chatbots, copilots, and automated content generation tools using OpenAI/Azure OpenAI and Hugging Face.

- Prompt Engineering: Implement and manage advanced prompt optimization and versioning workflows to enhance model accuracy.

- Monitoring & Evaluation: Establish robust evaluation frameworks (e.g., RAGAS, TruLens) to track model performance, hallucination rates, and drift in production.

- Operationalization: Collaborate with cross-functional teams (Product, DevOps, Data) to identify high-impact use cases and move them from POC to production.

- Best Practices: Set the standard for GenAI engineering, including model selection criteria, performance tracking, and ethical AI safeguards.

Required Skills & Qualifications :

- Technical Stack: Deep proficiency in Python and GenAI frameworks (LangChain, LlamaIndex).

- Vector Infrastructure: Hands-on experience with vector indexing, semantic search, and hybrid search strategies.

- Cloud Proficiency: Strong experience with Azure (Azure OpenAI, Azure Functions, Azure DevOps) or similar cloud ecosystems.

- Deployment: Experience with CI/CD for AI, MLOps, and containerization.

- Problem Solving: A proven track record of solving complex "hallucination" and data grounding issues in production environments.