We're seeking a Senior AI Engineer to architect and ship LLM-powered applications and agentic systems that address genuine challenges for our users and teams. Your work will span the entire applied-AI stack — from retrieval-augmented generation (RAG) pipelines and prompt design to multi-step agents that reason, leverage tools, and automate end-to-end workflows. This position is for a senior builder. You'll drive complex use cases from ambiguous problems through to production: selecting appropriate models, implementing agentic architectures, connecting retrieval and tooling, establishing quality evaluation methods, and delivering dependable applications at scale. Responsibilities Architect and deliver production LLM applications — chat, copilots, assistants, and autonomous workflows — from idea to scale Construct and implement RAG pipelines: chunking, embeddings, vector search, reranking, and grounding to minimize hallucination and boost relevance Create agentic architectures: multi-step reasoning, tool/function calling, planning, memory, and multi-agent orchestration Support the automation of business and engineering workflows through agentic AI and workflow automation Establish prompt and context strategies; construct evaluation harnesses and maintain quality, latency, and cost standards Connect LLMs with internal data, APIs, and tools through connectors, function calling, and structured outputs Deploy guardrails, safety, and observability for AI systems (tracing, evals, monitoring for quality and drift) Partner with product, data, and platform teams to transform ambiguous problems into shipped AI features Exchange knowledge with fellow engineers and take part in design reviews Requirements 3+ years in software or ML engineering, including recent, hands-on experience building and shipping applications with LLMs Demonstrated track record of delivering applied-AI systems end to end Proficiency in Python at an advanced level Background in building RAG systems — embeddings, retrieval, and reranking with vector databases (Pinecone, Qdrant, Milvus, or pgvector) Expertise in LLM APIs and orchestration frameworks (OpenAI, Anthropic, LangChain, or LlamaIndex) Skills in designing agentic architectures — tool use, function calling, planning loops, and agent orchestration in production Competency in automating workflows with agentic AI or workflow-automation tooling Knowledge of prompt engineering and structured/JSON output techniques Capability to design evaluations and reason about LLM quality, cost, and latency trade-offs at scale Strong command of written and spoken English (B2+ level) Nice to have Familiarity with multi-agent frameworks (LangGraph, CrewAI, or AutoGen) Background in fine-tuning, adapters (LoRA), or model distillation Understanding of MLOps/LLMOps — deployment, versioning, and monitoring of AI systems, including model serving and inference optimization Knowledge of AI safety, guardrails, and evaluation frameworks (Ragas, LangSmith, or promptfoo) Expertise in cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
