Build, iterate, and maintain AI agents within the architecture and boundaries defined by the Lead AI Engineer.
Integrate data components across media platforms — ingesting, normalizing to schema, and routing to the correct agent context.
Run prompt benchmarking, track output quality across model versions, and flag hallucination patterns or quality regressions proactively.
Build and maintain ETL/ELT pipelines supporting daily automated callouts and weekly optimisation reporting.
Operate within and extend the MCP connector library for external platform APIs.
Build and maintain Slack-based approval flows — agent callouts, feedback capture, exception alerts, and operational notifications.
Monitor output quality, respond to incidents, drive root-cause fixes rather than surface patches.

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

AI Engineer

Key skills