Requirements

4+ years across software, data engineering, ML, or AI platform work with direct ownership of production systems
Experience with media platform APIs (Google Ads, Meta, DV360, Semrush, SerpAPI)
Strong Python and SQL — production-grade, not just analytical scripts
MCP or equivalent integration layer experience
Hands-on experience building or operating LLM applications, agentic systems, or tool-calling workflows
Workflow orchestration tooling: Airflow, Dagster, Prefect, dbt
ETL/ELT pipeline design and data reliability in production — schema management, contract enforcement, freshness monitoring
Cloud infrastructure: AWS, GCP, or Azure; containerized deployments (Docker)
Experience defining evaluation frameworks and success criteria for model outputs
Slack API and webhook-based workflow automation
Familiarity with vector databases and RAG patterns for long-context data retrieval
Experience shipping systems that mix model logic, deterministic business rules, and human approval flows
LLM evaluation tooling — token cost tracking, hallucination detection, model benchmarking

THIS ROLE IS NOT

Generalist developers without LLM or agentic system experience
Prompt engineering work without responsibility for data quality and pipeline reliability
Low-code automation builders (n8n, Make, Zapier)
A role with loose evaluation standards or optional quality gates
Research-only AI scientists without production ownership
A role where shipping late with a perfect solution is acceptable
Candidates who need architecture decisions made for them before starting
A role without direct accountability for production incidents on your agents

Originally posted on Himalayas

AI Engineer

Summary