LLM / GenAI Engineer

About The Role The role is for someone who has moved beyond basic prompting and understands what it takes to build production-grade AI systems: robust RAG pipelines, agentic workflows, fine-tuning pipelines, and systematic evaluation frameworks. The engineering team owns complex pieces of a rapidly scaling AI platform, working directly with applied scientists and backend engineers to solve hard problems in generative AI. Key Responsibilities Design and implement production-grade RAG pipelines using LangChain, LlamaIndex, or custom architectures Build and optimize vector database integrations such as Pinecone, Weaviate, or pgvector for semantic search at scale Develop systematic LLM evaluation frameworks including benchmark suites, LLM-as-judge pipelines, and regression testing Run instruction fine-tuning and parameter-efficient fine-tuning workflows like LoRA and QLoRA on domain-specific datasets Deploy, monitor, and scale LLM applications using cloud infrastructure, containerization, and robust API design Write clean, testable, well-documented code and actively participate in architecture reviews and team engineering standards What We Are Looking For 3-6 years of software engineering experience, with at least 2 years focused specifically on LLMs and generative AI in production Deep familiarity with LLM orchestration frameworks such as LangChain or LlamaIndex Solid understanding of embedding models, vector databases, and semantic similarity in production environments Strong Python skills, with comfort in async programming, REST API design, and cloud infrastructure Bonus: Experience with model quantization, custom CUDA kernels, or contributing to open-source AI projects Show more Show less