Senior Software Engineer - AI and Automation, Data & Analytics
at Grafana Labs
USD 154,400-185,300 per year
Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
API @ 4
AWS @ 4
AWS Lambda @ 4
Audit @ 4
Communication @ 7
Compliance @ 4
Data Engineering @ 4
Experimentation @ 4
GCP @ 4
Grafana @ 4
JavaScript @ 7
LLM @ 4
LangChain @ 4
Marketing @ 4
Microservices @ 4
Node.js @ 7
Observability @ 4
Python @ 7
RAG @ 4
React @ 4
SQL @ 3
Salesforce @ 4
Slack @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Grafana Labs is seeking a Senior Software Engineer focused on building AI-powered internal tools and agentic workflows for GTM (Go-To-Market) teams. You will own technical infrastructure that powers automation across Sales, Customer Success, and Marketing—building agentic skills, backend services, and integrations that connect LLMs to business systems.
This is a remote opportunity; applicants must be in USA time zones only.
Responsibilities
- Own end-to-end development of agentic and AI-integrated workflows: design, implementation, testing, deployment, and maintenance
- Build modular, composable agentic systems using orchestration frameworks (examples: LangChain, CrewAI, Anthropic MCP)
- Develop reusable "agentic skills" for GTM teams that can be invoked across interfaces (Slack, dashboards, internal apps)
- Implement observability and feedback loops: logging, performance metrics, prompt iteration, and model evaluation
- Build MCP servers, CLIs, APIs and microservices that connect AI models to business systems (Salesforce, BigQuery, Slack, HubSpot, email, calendars, analytics tools)
- Architect data flows for retrieval-augmented generation (RAG) connecting LLMs to internal knowledge bases, BigQuery, Salesforce, and real-time context
- Build serverless or containerized services (GCP Cloud Functions, Cloud Run, or similar) that scale with usage and integrate with Grafana's cloud infrastructure
- Scope automation problems 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
- Produce documentation and enablement materials for self-service automation
- Partner with Data Engineering, GTM Analytics, Field Operations, and GTM Systems to source/structure data and prioritize use cases
- Establish governance and compliance standards for AI workflows (access controls, audit trails, human-in-the-loop escalation)
Requirements
- 5+ years of software engineering experience, including backend development and systems integration
- 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); able to query, structure, and pipeline data for AI workflows
- Experience with authentication patterns, secure API handling, and rate limiting
- Proven ability to deliver AI-powered features in production
- Ability to identify high-leverage initiatives and balance experimentation with engineering rigor
- Strong communication skills for cross-functional collaboration and explaining technical trade-offs
- Comfortable working autonomously in ambiguous, fast-moving projects
Bonus
- Frontend experience (React, Slack Block Kit, dashboard components) for building user-facing AI tools
- Familiarity with GTM platforms like Salesforce, HubSpot, Outreach, Gainsight
- Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, pgvector)
- Prior work automating sales, customer success, or marketing workflows in a B2B SaaS environment
- Experience with workflow automation platforms (n8n, Prefect, Clay, PhantomBuster, Apify, Dust)
- Familiarity with Model Context Protocol (MCP) or similar standards
- Exposure to observability tools for AI systems (LangSmith, Weights & Biases, custom logging/evaluation frameworks)
- Experience in open source or developer-focused SaaS companies
- Familiarity with graph databases for RAG systems (Neo4J, Memgraph, Puppygraph)
Compensation & Rewards
- Base compensation range (United States): USD 154,445 - USD 185,334
- Roles include Restricted Stock Units (RSUs), bonus (if applicable), and other benefits
Why You’ll Thrive at Grafana Labs
- Remote-first company with a global culture
- Opportunity to work in a scaling organization with transparent communication and autonomy
- Open-source roots and a developer-focused environment
- In-person onboarding and a global annual leave policy (30 days per annum, with 3 days reserved for Grafana Shutdown Days)
Equal Opportunity Employer
Grafana Labs recruits, trains, compensates and promotes regardless of race, religion, color, national origin, gender, disability, age, veteran status, and other characteristics. The company may utilize AI tools in its recruitment process, with manual review by the recruitment team.
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