Senior AI Engineer

Role Overview

As a Senior AI Engineer at Grafana Labs, you will own the AI agent infrastructure and automation platform that powers the Marketing Operations organization. This is a high-autonomy role focused on designing production-ready AI systems that connect models to internal and third-party data.

Key Missions

• Design and implement multi-agent architectures, including modular and composable systems that can run 24/7.
• Build LLM integrations and backend services connecting AI models to internal and external data platforms.
• Identify and solve high-impact automation problems in marketing and sales operations, delivering solutions with measurable business outcomes.

Responsibilities

• Create multi-agent solutions using orchestration frameworks and patterns (e.g., sequential chains, router/dispatcher, parallel fan-out).
• Implement state management and ensure production monitoring for AI agent systems.
• Integrate AI systems with data sources and automation workflows, operating reliably at scale.
• Diagnose business problems and translate them into workflows, outcomes, and technical implementations.

Requirements

• Ability to diagnose business problems before writing code; think in workflows and outcomes.
• Proven track record delivering end-to-end with minimal direction and focusing on high-leverage work.
• Fluent with AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code).
• Hands-on experience applying LLM frameworks and patterns, including:
  • Prompt engineering
  • RAG
  • Function calling/tool use
  • Structured output parsing
  • Evaluation
• Strong proficiency in Python and JavaScript/Node.js, with Git-based workflows, code review, and testing discipline.
• 2+ years applying LLMs/AI to production workflows (not just prototypes).
• Experience building and operating multi-agent systems at scale, including orchestration, state management, and production monitoring.
• Understanding of LLM failure modes and production mitigations (e.g., confidence thresholds, fallback logic, human escalation, cost/latency management).
• Deep familiarity with Google Cloud Platform, including BigQuery and serverless/containerized services (Cloud Functions, Cloud Run).
• Ability to explain complex systems to both engineers and business stakeholders.

Nice-to-Haves

• Experience with vector databases / retrieval pipelines (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector).
• Familiarity with marketing/sales platforms (Salesforce, Customer.io, HubSpot, Marketo, Outreach).
• Observability and evaluation tooling for AI systems (LangSmith, Weights & Biases, custom evaluation frameworks).
• Experience building user-facing AI interfaces with React and/or Slack Block Kit.
• Workflow orchestration platforms (n8n, Temporal, Prefect, Airflow).
• Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources.
• Background automating marketing/sales/customer success workflows in B2B SaaS.
• Active participation in open-source communities.