3 months ago
Base Salary
$154k - $185k/yr
Responsibilities
- Own end-to-end development, deployment, testing, and operation of multi-agent AI systems.
- Build modular agentic systems, reusable agent skills, and integrations across Slack, dashboards, internal applications, and CLIs.
- Define the automation platform’s technical direction, including data models, API contracts, shared libraries, and reference architectures.
- Implement observability, performance metrics, prompt iteration, model evaluation, feedback loops, and cost management.
- Establish AI workflow governance and compliance practices covering access controls, audit trails, PII handling, and human escalation.
- Build MCP servers, APIs, CLIs, microservices, and RAG data flows connecting AI models to business systems and knowledge bases.
- Deploy scalable serverless and containerized services using Google Cloud infrastructure.
- Partner with RevOps, Demand Generation, Regional Marketing, SDR, Data Engineering, GTM Systems, and Field Operations teams to deliver measurable automation outcomes.
- Design and deploy reliable workflows using n8n, Workato, or custom platforms.
- Create documentation, playbooks, and enablement materials that allow partner teams to operate automations independently.
Requirements
- 8+ years of software engineering experience with depth in backend development, systems integration, or data and analytics engineering.
- 2+ years of hands-on experience applying LLMs or AI to production workflows.
- Strong proficiency in Python and JavaScript/Node.js, with Git-based workflows, code review, and testing experience.
- Experience with LLM frameworks and patterns including prompt engineering, RAG, function calling, structured output parsing, and evaluation.
- Experience building and operating multi-agent systems at scale, including orchestration, state management, and production monitoring.
- Deep familiarity with Google Cloud Platform, BigQuery, Cloud Functions, and Cloud Run.
- Understanding of LLM failure modes and mitigations such as confidence thresholds, fallback logic, human escalation, and cost and latency management.
- Ability to identify high-leverage problems, work independently, and communicate complex systems to technical and business stakeholders.
- Experience with vector databases or retrieval pipelines such as Pinecone, Weaviate, ChromaDB, Qdrant, or pgvector is preferred.
- Familiarity with Salesforce, Customer.io, HubSpot, Marketo, or Outreach is preferred.
- Experience with React, Slack Block Kit, LangSmith, Weights & Biases, n8n, Temporal, Prefect, Airflow, or Model Context Protocol is preferred.
- Prior experience automating marketing, sales, or customer success workflows in a B2B SaaS environment and active open-source participation are preferred.
Benefits
- 100% remote opportunity for candidates in the U.S.
- In-person onboarding is provided.
- 30 days of annual leave per annum, including 3 Grafana Shutdown Days.
- Career growth pathways and an innovation-driven, high-trust culture.
- RSUs are included for all roles.
Tech Stack
Categories
About Grafana
Grafana Labs builds open-source observability tools and a managed SaaS, Grafana Cloud, used by engineering and SRE teams to monitor, visualize, and analyze telemetry. Its products include Grafana dashboards plus Loki (logs), Tempo (traces), and Mimir (metrics), offered as cloud subscriptions and enterprise software. Privately held and headquartered in New York, it serves customers such as Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce.