Developer Advocate

Overview

You create technical content that demonstrates how TrueFoundry's AI gateway and orchestration platform works. Your audience is senior engineers and platform teams evaluating infrastructure for multi-agent AI systems. You're writing code, building demos, and producing tutorials that show how to route between models, orchestrate tools via MCP (Model Control Protocol), and manage complex AI workflows in production.

Role Snapshot

Company Context

Stage: Series A/B (100 employees suggests meaningful funding and traction)

Size: 100 employees

Growth: Actively hiring for GTM roles, building out developer relations function

Market Position: Category creator in AI infrastructure—the problem they're solving (orchestrating multi-agent systems) is emerging as companies move beyond simple LLM wrappers

GTM Reality

Pipeline Sources:

• 40% Developer-led: Engineers find content, test the product, pull in their teams
• 30% Outbound: Sales team targets platform/ML engineering leaders at enterprises
• 30% Community/Events: Conference talks, open-source contributions, developer community engagement

How This Role Fits: You're the top-of-funnel for technical audiences. Sales can't close these deals without engineering buy-in, and engineers won't evaluate a product unless the technical story is clear and the content is trustworthy. Your demos and tutorials determine whether a prospect moves from "maybe" to POC.

Success Metric: Developers who engage with your content and then sign up for trials or request technical deep-dives.

Competitive Landscape

Main Competitors: Unknown (category is nascent), but likely competing against:

• Build-it-yourself solutions (AWS Bedrock + custom orchestration)
• LangChain/LlamaIndex deployment patterns
• Other emerging AI infrastructure platforms

How They Differentiate: Enterprise-grade governance, observability, and security for multi-agent systems. Not just "run an LLM" but orchestrate complex workflows with tool usage, memory, and multi-step reasoning.

Common Objections:

• "Why not just use LangChain and deploy ourselves?"
• "How is this different from [generic LLM platform]?"
• "Can we see production examples at scale?"

Win Themes:

• Enterprise compliance/security that homegrown solutions lack
• Observability and governance across complex agent workflows
• Faster time-to-production vs building infrastructure yourself

What You'll Actually Do

Time Breakdown

Content Creation (50%) | Community Engagement (25%) | Internal Collaboration (25%)

Key Activities

• Build Working Demos: You write actual code that shows multi-agent architectures using TrueFoundry. These aren't toy examples—they need to demonstrate real production patterns like routing between models, managing tool orchestration via MCP, handling context and memory across agent steps. You're testing the product yourself and finding edge cases.

• Write Technical Tutorials: You produce step-by-step guides for specific use cases (e.g., "Building a Multi-Agent Customer Support System" or "Orchestrating RAG Pipelines with Tool Calling"). These live on the blog, in docs, and on developer platforms like Dev.to or Medium. You're explaining architectural decisions, not just API calls.

• Create Video/Livestream Content: You do live coding sessions or recorded walkthroughs showing how to implement something with TrueFoundry. This means being comfortable on camera, talking through technical decisions in real-time, and handling questions from developers in chat.

• Engage with the Community: You're active on Twitter/X, LinkedIn, Discord, and wherever AI engineers hang out. You answer technical questions, share insights about AI infrastructure, and represent TrueFoundry in developer conversations. You attend conferences and may give talks or run workshops.

• Collaborate with Sales/Product: When a big enterprise prospect is evaluating the platform, sales pulls you in to create a custom demo or address specific technical concerns. You work with product to understand roadmap and translate upcoming features into compelling narratives for developers.

The Honest Reality

What's Hard

• You're Creating Content for a Moving Target: AI infrastructure is evolving fast. The MCP protocol you're documenting today might work differently in three months. Your tutorials need updates, your demos break when APIs change. This isn't "write once and move on" content.

• Measuring Impact is Fuzzy: You can track views and engagement, but connecting your tutorial to a specific deal closing 6 months later is nearly impossible. Leadership will ask "is this working?" and the answer is often "probably, but hard to prove."

• You're Constantly Context-Switching: One day you're writing Python code for a RAG demo, the next you're on a Zoom with a prospect explaining MCP architecture, then you're filming a video tutorial. It's varied but can feel scattered. Hard to get into deep flow state.

• Technical Credibility is Everything: If you publish a demo that doesn't work or make an architectural recommendation that's questionable, developers will dismiss you (and the company) immediately. The bar for quality is high and unforgiving.

What Success Looks Like

• Developers reference your tutorials when evaluating TrueFoundry against competitors
• Your content gets shared organically in AI engineering communities
• Sales team regularly sends prospects your demos and gets positive feedback
• Product team uses your feedback to prioritize features that matter to developers

Who You're Influencing

Primary Audience:

• Senior/Staff Engineers building AI systems
• ML Platform Engineering teams
• AI/ML Engineering Managers evaluating infrastructure

What They Care About:

• Does this actually work in production at scale?
• How much complexity does this abstract away vs create?
• Can I see real code and architectures, not marketing fluff?
• What's the tradeoff vs building this ourselves?
• How does this integrate with our existing ML stack?

Requirements

• You've shipped production code—preferably with AI/ML systems, LLMs, or infrastructure tooling
• You can write and speak clearly about complex technical concepts without dumbing them down or over-complicating
• You have a portfolio of technical content (blog posts, talks, GitHub projects, videos) that developers actually engaged with
• You're comfortable building demos in Python, working with LLM APIs, and understanding distributed systems concepts
• You understand what makes good developer documentation vs what makes bad documentation (and can articulate why)
• You're self-directed enough to identify what content needs to exist without someone telling you exactly what to create