AI Engineer — Agentic Systems

Requirements

• 5+ years of professional software, ML, or data engineering experience, including at least 1 year building LLM-based or GenAI applications.
• Strong Python engineering skills - not just notebooks. Production code, testing, packaging, async patterns, and API development with FastAPI or similar.
• Hands-on experience with at least one LLM orchestration framework (LangChain, LangGraph, LlamaIndex, Semantic Kernel, or an equivalent) in a shipped application.
• Demonstrated experience building RAG systems: embeddings, vector stores (e.g., FAISS, pgvector, Pinecone, Chroma), and retrieval quality tuning.
• Practical command of prompt engineering, structured output, function/tool calling, and context-window management.
• Working knowledge of SQL and cloud data platforms (Snowflake, Databricks, or equivalent), and comfort writing queries against enterprise schemas.
• Experience containerizing and deploying services with Docker and shipping through CI/CD on a major cloud (Azure, AWS, or GCP).
• Solid grounding in ML fundamentals and familiarity with PyTorch, TensorFlow, or HuggingFace Transformers.
• Clear written and verbal communication, and the judgment to escalate when a problem is under-specified rather than guessing.
• Preferred: Experience building or consuming Model Context Protocol (MCP) servers, or comparable agent tool-integration layers.
• Preferred: Production experience with multi-agent or agentic workflow systems, including human-in-the-loop checkpoints and approval gates.
• Preferred: Familiarity with LLM observability and evaluation tooling such as LangSmith, Langfuse, Weights & Biases, or MLflow.
• Preferred: Experience with workflow orchestration tools - Airflow, Dagster, Prefect - or analytics engineering with DBT.
• Preferred: Exposure to model fine-tuning or parameter-efficient adaptation (LoRA/PEFT) and knowing when it is - and is not - the right answer.
• Preferred: Background in a technical engineering domain (semicondu

Location

• Austin, TX