Hands-on engineering experience, with building something real with LLMs, RAG, agents, or GenAI applications. Substantial hobby projects count.
Strong Python. Comfortable with LangChain, LlamaIndex, LangGraph, DSPy, or equivalents, and clear on when to use them versus when to write plain code.
Practical experience with at least one major cloud (AWS, Azure, or GCP) and with vector databases such as Pinecone, Weaviate, pgvector, or similar.
Solid data engineering fundamentals. SQL, working with messy enterprise data, building ETL pipelines, and handling structured and unstructured data.
Working knowledge of model evaluation, prompt engineering, fine-tuning approaches such as LoRA and SFT, and the tradeoffs between closed and open-weight models.
Familiarity with MLOps and LLMOps tooling such as MLflow, Weights and Biases, LangSmith, or similar observability stacks.
Exposure to deploying in regulated environments (financial services, healthcare, public sector) is a strong plus.
Prior experience as a Forward Deployed Engineer, Solutions Architect, or Applied AI Engineer for a client.
Contributions to open-source AI projects or a public portfolio of GenAI work.
Experience building agentic systems in production, including tool use, multi-agent orchestration, and human-in-the-loop design.
