LLM / GenAI Engineer

About The Role The role is for someone who has moved beyond prompting and understands what it takes to build production-grade AI systems: RAG pipelines, agentic workflows, fine-tuning pipelines, and systematic evaluation frameworks. You will own complex pieces of an AI platform and work directly with applied scientists, backend engineers, and enterprise clients to ship features that are genuinely relied on in production. Key Responsibilities Design and implement RAG pipelines using LangChain, LlamaIndex, or custom architectures - including chunking strategies, embedding selection, and retrieval quality optimizationBuild and optimize vector database integrations (Pinecone, Weaviate, Chroma, pgvector) for semantic search at production scaleDevelop systematic LLM evaluation frameworks: benchmark suites, LLM-as-judge pipelines, regression testing, and hallucination detectionRun instruction fine-tuning and parameter-efficient fine-tuning (LoRA, QLoRA) on domain-specific datasetsCollaborate with backend engineers to integrate LLM capabilities into production APIs with appropriate latency, cost, and reliability constraintsTrack and synthesize relevant LLM research and translate high-value advances into product featuresWrite observable, tested, and well-documented code; participate in architecture reviews and production incident response What We Are Looking For 2–5 years of software engineering experience, including at least 1 year working with LLMs in a production or near-production contextDeep familiarity with at least one LLM orchestration framework: LangChain, LlamaIndex, or equivalentHands-on experience with OpenAI, Anthropic, Google Gemini, or open-source model APIs at meaningful scaleUnderstanding of embedding models, vector databases, and semantic similarity in production environmentsStrong Python skills; comfort with async programming, REST API design, and cloud infrastructure (AWS/GCP/Azure)MS or BS in Computer Science, AI, or related field; equivalent demonstrable experience welcomedBonus: fine-tuning experience (LoRA/QLoRA), RLHF pipeline exposure, multi-agent system design, or contributions to open-source AI tooling Location San Francisco Bay Area (Hybrid) New York City Austin, TX Remote strongly considered