Job Title : -Lead Generative AI Engineer / Player-Coach
Purpose : Own our Generative AI technical vision. You will rapidly prototype and lead a dedicated team of two engineers to launch our company's first intelligent search and content automation systems.
Role Summary
We're looking for a hands-on Gen AI pioneer who can architect, code, and mentor. This is a "player-coach" role where you'll be building foundational systems while guiding your team. You will partner daily with product and engineering leadership to transform business goals into cutting-edge, shippable LLM-powered solutions .
Key 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.Nice-to-Have Skills
Experience with agentic workflows (e.g., AutoGen, CrewAI).Familiarity with multi-modal models (text, image, etc.).Knowledge of advanced LLM fine-tuning techniques (e.g., LoRA, QLoRA).Strong SQL skills (especially with ClickHouse) and a keen eye for inference cost optimization .