Senior AI Engineer
Senior Engineer building multi-agent AI systems, LLM integrations, and backend automation services that power Marketing Operations. Owns technical direction for agentic infrastructure connecting models to business systems.
About the job
Agentic Systems & AI Infrastructure
- Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation
- Build modular, composable agentic systems using orchestration frameworks (LangChain, CrewAI, Anthropic MCP, or similar) that operate 24/7 across teams
- Develop reusable agentic skills that agents invoke across interfaces (Slack, dashboards, internal apps, CLIs)
- Implement observability and feedback loops including logging, performance metrics, prompt iteration, model evaluation, and cost management
- Establish governance and compliance standards for AI workflows including access controls, audit trails, PII handling, and human-in-the-loop escalation paths
Systems Integration & Backend Services
- Build MCP servers, APIs, CLIs, and microservices connecting AI models to business systems (BigQuery, Slack, CRMs, email, calendars, analytics tools)
- Architect data flows for retrieval-augmented generation (RAG), connecting LLMs to internal knowledge bases, customer data, and real-time business context
- Build serverless or containerized services (GCP Cloud Functions, Cloud Run) that scale with usage and integrate with Grafana's cloud infrastructure
Automation & Workflow Enablement
- Partner with RevOps, Demand Generation, Regional Marketing, and SDR teams to scope high-impact automation problems, identify bottlenecks, and build solutions with measurable business outcomes
- Design and deploy workflows using orchestration tools (n8n, Workato, or custom platforms) with CI/CD, testing, and production reliability standards
- Build systems designed for self-service with documentation, playbooks, and enablement materials that let partner teams operate independently
What Makes You a Great Fit
- 8+ years of software engineering experience with depth in backend development, systems integration, or data/analytics engineering
- 2+ years hands-on experience applying LLMs/AI to production workflows, not just prototypes
- Strong proficiency in Python and JavaScript/Node.js with Git-based workflows, code review practices, and testing discipline
- Hands-on experience with LLM frameworks and patterns including prompt engineering, RAG, function calling/tool use, structured output parsing, and evaluation
- Experience building and operating multi-agent systems at scale including agent decomposition, orchestration patterns (sequential chains, router/dispatcher, parallel fan-out), state management, and production monitoring
- Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services (Cloud Functions, Cloud Run)
- Understanding of LLM failure modes and production mitigations including confidence thresholds, fallback logic, human escalation, and cost/latency management
- Fluent with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code)
Bonus Points
- Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector)
- Familiarity with marketing or sales platforms (Salesforce, Customer.io, HubSpot, Marketo, Outreach)
- Experience with frontend frameworks (React, Slack Block Kit) for building user-facing AI tool interfaces
- Observability tooling for AI systems (LangSmith, Weights & Biases, custom evaluation frameworks)
- Experience with workflow orchestration platforms (n8n, Temporal, Prefect, Airflow)
- Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources
- Prior work automating marketing, sales, or customer success workflows in a B2B SaaS environment
Skills
Python, JavaScript, Node.js, LangChain, Crewai, Anthropic Mcp, RAG, BigQuery, Gcp Cloud Functions, Cloud Run, Git, Github Copilot, Cursor, Claude Code, Pinecone
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