AI Engineer

This is a fully remote position, open to applicants in Europe.

📋 Description

• Develop, refine, and sustain AI agents within the frameworks and guidelines established by the Lead AI Engineer.

• Integrate data elements across various media platforms by ingesting, normalizing to schema, and directing to the appropriate agent context.

• Conduct prompt benchmarking, monitor output quality across different model versions, and proactively identify hallucination patterns or quality regressions.

• Construct and manage ETL/ELT pipelines that facilitate daily automated callouts and weekly optimization reporting.

• Work within and enhance the MCP connector library for external platform APIs.

• Create and uphold Slack-based approval workflows — including agent callouts, feedback collection, exception alerts, and operational notifications.

• Oversee output quality, address incidents, and focus on root-cause solutions instead of temporary fixes.

⛳️ Requirements

• A minimum of 4 years of experience in software, data engineering, ML, or AI platform work, with direct responsibility for production systems.

• Familiarity with media platform APIs such as Google Ads, Meta, DV360, Semrush, and SerpAPI.

• Proficiency in Python and SQL — capable of producing production-grade code rather than just analytical scripts.

• Experience with MCP or a similar integration layer.

• Practical knowledge in building or managing LLM applications, agentic systems, or tool-calling workflows.

• Experience with workflow orchestration tools like Airflow, Dagster, Prefect, or dbt.

• Expertise in designing ETL/ELT pipelines and ensuring data reliability in production, including schema management, contract enforcement, and freshness monitoring.

• Proficient in cloud infrastructure: AWS, GCP, or Azure; experience with containerized deployments (Docker).

• Ability to define evaluation frameworks and success criteria for model outputs.

• Knowledge of Slack API and webhook-based workflow automation.

• Familiarity with vector databases and RAG patterns for long-context data retrieval.

• Experience in deploying systems that integrate model logic, deterministic business rules, and human approval workflows.

• Competence in LLM evaluation tools — including token cost tracking, hallucination detection, and model benchmarking.

🏝️ Benefits

• Health insurance

• Flexible work arrangements

• Professional development opportunities

• Remote work options

• Performance-based culture