8+ years of software engineering experience building scalable systems, reusable tools and frameworks, with 3+ years taking LLM or agentic systems from prototype to production, and deep fluency in the modern AI stack. You architect, build and operate production-grade AI products composed of LLMs, foundation models, agents and deterministic components, for both human and machine consumption, with clear judgment on inference-versus-compute boundaries, task decomposition across specialized models, orchestration of multi-step reasoning and tool use, and graceful degradation under failure.
Solid foundation in machine learning and deep learning. You understand how modern models (transformers, LLMs) are trained, fine-tuned and evaluated, reason about embeddings, loss functions and statistical rigor, and can diagnose whether a production issue is prompt, retrieval, model or data. Proficiency in at least one high-level language (Python, Scala, Java, or Go), and the discipline to write code that is readable, observable in production, and testable at the boundaries.
Hands-on fluency with modern LLM and agent frameworks (LangChain, LlamaIndex, Semantic Kernel, Google ADK or equivalent), vector databases (FAISS, Chroma or similar), and agentic architectures, multi-agent coordination, tool invocation and stateful reasoning. You've moved beyond vanilla RAG and embeddings, knowing where they help, where they break, and when to reach for planning, reranking, structured reasoning, fine-tuning or deterministic compute instead.
Production discipline for AI systems: evaluation harnesses, guardrails and telemetry that change decisions (offline evals, golden sets, LLM-as-judge, behavioral regression, drift monitoring); and optimization for cost, latency, throughput and inference quality (model selection, serving decisions, token-spend control, caching, batching, streaming, distillation, quantization, speculative decoding). Experience with the data infrastructure ecosystem, SQL engines (such as Trino, Presto or Spark), lakehouse architectures, workflow orchestration, and streaming systems, and the ability to build AI capabilities that sit natively on top of it.
A strategic product mindset paired with a research sensibility. You read papers, separate signal from hype, tackle loosely defined problems with meticulous attention to detail, and drive ambiguous projects to completion in a fast-paced dynamic environment without sacrificing trust. You communicate clearly across cross-functional teams to influence product strategy, and you evangelize AI engineering practices through workshops, technical playbooks, design guidance, and mentorship that raises the AI fluency of partner organizations.
MS or BS in Computer Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics or a related field OR equivalent practical experience building AI systems in production.