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 high-scale AI platform, working directly with applied scientists and backend engineers to deploy reliable generative models. Key Responsibilities Design and implement production-grade RAG pipelines using LangChain, LlamaIndex, or custom orchestration architectures Build and optimize vector database integrations including Pinecone, Weaviate, or pgvector for semantic search at scale Develop systematic LLM evaluation frameworks incorporating benchmark suites, LLM-as-judge pipelines, and regression testing Execute instruction fine-tuning and parameter-efficient fine-tuning techniques such as LoRA and QLoRA on domain-specific datasets Integrate monitoring and observability tools like LangSmith or Arize to track token usage, latency, and output drift Write clean, testable, and well-documented Python code while participating in rigorous architecture reviews 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, prompt engineering best practices, and embedding models Strong Python skills with comfort in async programming, REST API design, and cloud infrastructure Solid understanding of ML fundamentals, tokenization, context windows, and vector similarity search Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or equivalent practical experience Bonus: Experience contributing to open-source AI projects or published research in NLP and generative modeling Show more Show less