You'll design and ship AI systems and automation workflows that solve real business problems. You know when to reach for an LLM and when a simple rule or lookup table does the job better — and you build accordingly.
Experience with data engineering is a meaningful plus: DAGs, ETL pipelines, and agent workflows share the same core challenges around reliability, observability, and failure handling.
You'll Build ETL/ELT pipelines and orchestrated automation workflows
AI pipelines with LLMs, embeddings, and retrieval where they add real value
RAG systems and agents grounded in real company data
Integrations that connect AI outputs to actual business tools and processes
What We're Looking For
Core:
Python, LLM APIs (OpenAI, Anthropic, etc.), prompt engineering and evaluation
RAG pipelines, embeddings, vector databases
Workflow automation and orchestration — agents, triggers, conditional logic
Strong judgment on when to use AI vs. deterministic logic
Big plus: ETL/ELT pipeline design, SQL, data modeling, API integrations
Airflow, Prefect, dbt, BigQuery/Snowflake, cloud (GCP/AWS/Azure)
Bonus: LangChain/LlamaIndex · n8n/Make/Zapier · Docker
You Likely...
build things from scratch and care about reliability, not just demos
have a data engineering or early-stage startup background
move fast, ship often, iterate on results
