Key Roles & Responsibilities :

- Architect & Build RAG Systems : Design, develop, and deploy sophisticated Retrieval-Augmented Generation (RAG) systems to power our next-generation search and discovery experience.

- Develop & Fine-Tune LLMs : Lead the development of advanced generative models for nuanced tasks like automated content creation, summarization, and metadata enrichment.

- Own the Gen AI Stack : Select, provision, and optimize our stack, leveraging managed services like Azure OpenAI or AWS Bedrock, or self-hosting models on GPU infrastructure. You will establish best practices for repo structure, CI/CD, and model/prompt versioning.

- Implement LLMOps : Embed robust observability using tools like OpenTelemetry and Prometheus. This includes tracking standard metrics (latency, cost, accuracy) and specialized monitoring for hallucination, toxicity, and data drift.

- Lead & Mentor : Hire, coach, and develop ML talent. Set the standard for high-quality code, rigorous experimentation, and rapid iteration within the Gen AI domain.

Must-Have Skills :

- Production LLM Experience : 5+ years in Python with demonstrable success in productionizing LLM applications using modern frameworks like DSPY, LangChain, LlamaIndex, or Hugging Face Transformers.

- RAG Expertise : Deep, practical knowledge of RAG architecture, including advanced prompt engineering, chunking strategies, and proficiency with vector databases (e.g., Pinecone, Weaviate, Milvus).

- Cloud Proficiency : Expertise with managed LLM services (Azure OpenAI Service or AWS Bedrock). Strong foundational cloud skills in either Azure or AWS for compute orchestration (AKS/EKS), serverless functions, and storage.

- MLOps Acumen : Solid experience with Docker, CI/CD pipelines (e.g., GitHub Actions, Argo), and model registries.

- Leadership & Communication : Proven ability to lead small, highly technical teams and clearly communicate complex concepts to stakeholders.