What You’ll Be Doing
Agentic Tool Development
- Own end-to-end development of agentic and AI-integrated workflows: design, implementation, testing, deployment, and maintenance
- Build modular, composable agentic systems using frameworks like LangChain, CrewAI, Anthropic MCP, or similar orchestration libraries
- Develop "agentic skills" for SDR and CSM teams—reusable capabilities that agents can invoke across interfaces (Slack, dashboards, internal apps)
- Implement observability and feedback loops: logging, performance metrics, prompt iteration, and model evaluation
Systems Integration & Backend Services
- Build MCP servers, CLIs and APIs and microservices that connect AI models to business systems: Salesforce, BigQuery, Slack, HubSpot, email, calendars, analytics tools
- Architect data flows that enable retrieval-augmented generation (RAG): connecting LLMs to internal knowledge bases, customer data in BigQuery, Salesforce data, and real-time business context
- Build serverless or containerized services (GCP Cloud Functions, Cloud Run, or similar) that scale with usage and integrate with Grafana's cloud infrastructure
GTM Automation & Workflow Manufacturing
- Scope high-impact automation problems autonomously by shadowing Sales, Customer Success, and Marketing teams to identify efficiency gaps
- Design and deploy automation workflows using tools like n8n, Zapier, Prefect, or custom orchestration platforms
- Build systems designed for self-service, with documentation and enablement materials that let others operate them independently
Collaboration & Technical Leadership
- Partner with GTM Analytics, Field Operations, Strategy & Planning, and GTM Systems teams to scope, prioritize, and refine use cases
- Collaborate with Data Engineering to source and structure relevant data for agentic and AI-enabled workflows
- Communicate technical constraints and trade-offs clearly to non-technical stakeholders across Revenue Operations and GTM leadership
- Establish and champion governance and compliance standards to AI workflows, including access controls, audit trails, and human-in-the-loop escalation path
What Makes You a Great Fit / Requirements
- 5+ years of software engineering experience, including backend development and systems integration work
- Strong proficiency in Python (preferred) or Javascript/Node.js
- Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, or similar) and orchestration libraries (LangChain, LlamaIndex, Anthropic MCP, Semantic Kernel, etc.)
- Comfortable building internal APIs, microservices, or serverless systems (GCP Cloud Functions, Cloud Run, AWS Lambda, or similar)
- Familiarity with SQL and data warehouses (BigQuery preferred)—you understand how to query, structure, and pipeline data for AI workflows
- Experience with authentication patterns, secure API handling, rate limiting, and workflow automation
- Proven ability to deliver AI-powered features in production environments—you've shipped tools that real users depend on
- Strong problem selector who can identify high-leverage initiatives and push back on low-impact requests
- Thrives in ambiguous, fast-moving projects; able to balance experimentation with engineering rigor
- Clear technical communicator—you can explain complex systems in simple terms and collaborate effectively with product and data stakeholders
- Comfort with autonomy—you identify the right questions, structure unstructured problems, and drive work independently
Bonus Points For
- Experience with frontend frameworks & tooling (React, Slack Block Kit, dashboard components) to build user-facing interfaces for AI tools
- Familiarity with GTM platforms like Salesforce, HubSpot, Outreach, Gainsight, or similar CRM/sales engagement tools
- Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, pgvector, or similar)
- Prior work automating sales, customer success, or marketing workflows in a B2B SaaS environment
- Experience with workflow automation platforms like n8n, Prefect, Clay, PhantomBuster, Apify, Dust, or similar tools
- Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources and tools
- Exposure to observability tools for AI systems (LangSmith, Weights & Biases, custom logging/evaluation frameworks)
- Experience working in Revenue Operations, GTM Analytics, or Sales Operations environments
- Previous experience in open source or developer-focused SaaS companies
- Familiarity with or exposure to graph databases and their use in RAG systems (Neo4J, Memgraph, Puppygraph)
Compensation & Rewards
In the United States, the Base compensation range for this role is USD 154,445 - USD 185,334. Actual compensation may vary based on level, experience, and skillset as assessed in the interview process. Benefits include equity, bonus (if applicable) and other benefits.
Grafana LabsUnited States
