AI Engineer

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Description

Обязанности: Agent Development: Build, iterate, and maintain AI agents within the architecture and boundaries defined by the Lead AI Engineer. Own your agents end-to-end: prompt design, tool wiring, context routing, failure handling, and output validation., Data Integration: Integrate data components across media platforms — ingesting, normalizing to schema, and routing to the correct agent context. Work within defined data contracts and surface schema drift before it becomes a runtime failure., Evaluation-First Development: No feature enters development without defined success criteria and regression tests. Run prompt benchmarking, track output quality across model versions, and flag hallucination patterns or quality regressions proactively. Evaluation is not a post-build step., Pipeline & ETL Work: Build and maintain ETL/ELT pipelines supporting daily automated callouts and weekly optimisation reporting. Own data freshness and pipeline reliability for the agents you are responsible for., MCP Connector Work: Operate within and extend the MCP connector library for external platform APIs. Handle rate limits, retries, and failure modes — connectors must be resilient in production, not just in testing., Human-in-the-Loop Workflows: Build and maintain Slack-based approval flows — agent callouts, feedback capture, exception alerts, and operational notifications. These are the primary interface between the AI system and human decision-makers., Production Reliability: Own the reliability of your agents in production. Monitor output quality, respond to incidents, drive root-cause fixes rather than surface patches. Alert the Lead AI Engineer early on scope or complexity that affects delivery. Опыт: 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.

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