LLM / GenAI Engineer
About The Role The role focuses on building and shipping production LLM and GenAI systems: retrieval-augmented generation pipelines, agentic workflows, fine-tuning, and the evaluation infrastructure required to prove those systems actually work at scale. This is a hands-on engineering position on a well-funded AI team — expect to own significant components of the platform end-to-end, from model selection and prompt architecture to latency, cost, and reliability in production.
Key Responsibilities
Design and build RAG pipelines using LangChain, LlamaIndex, or custom architectures, including chunking strategies, hybrid retrieval, and reranking Develop agentic workflows with tool calling, planning, and multi-step reasoning — deployed behind robust APIs with strict latency and cost budgets Run parameter-efficient fine-tuning (LoRA, QLoRA, DPO) on domain datasets; manage experiment tracking, dataset versioning, and model registries Build systematic LLM evaluation frameworks: golden datasets, LLM-as-judge pipelines, offline benchmarking, and pre-deployment regression testing Own vector database infrastructure (Pinecone, Weaviate, pgvector, or Qdrant) including embedding refresh strategies and index performance tuning Optimize inference performance: quantization, batching, caching, and serving via vLLM, TGI, or managed APIs Collaborate with product and applied science teams to translate ambiguous business problems into scoped, shippable LLM features What We Are Looking For 3–6 years of software engineering experience, including at least 1–2 years shipping LLM/GenAI systems to production Strong Python engineering skills: async programming, API design, testing, and working with frameworks like FastAPI Hands-on experience with at least one major cloud (AWS, GCP, or Azure) and containerized deployment (Docker, Kubernetes) Practical knowledge of transformers, embeddings, tokenization, and the tradeoffs between hosted APIs and self-hosted open models (Llama, Mistral, Qwen) Experience operating vector databases and semantic search systems under real traffic BS/MS in Computer Science, related field, or equivalent practical experience Bonus: Experience with RLHF pipelines, multimodal models, structured output decoding (JSON mode, function calling at scale), or contributions to open-source LLM tooling