Senior Software Engineer, Agentic AI

AI is reshaping every industry on earth, and right now, almost no one has figured out how to govern it. Trustible exists to meet that challenge. We're building the platform that lets the world's largest organizations move fast with AI and prove they're doing it responsibly.

We're an AI-native Public Benefit Corporation headquartered in Arlington, Virginia, backed by over $6M in venture funding from investors betting on trustworthy AI as one of the defining infrastructure categories of the decade. We've grown fast, we're winning enterprise customers in some of the most impactful industries in the world, and we're still small enough that a senior engineer here shapes real architecture decisions, not just tickets.

We're hiring a Senior Software Engineer to build out Trustible's agentic AI capabilities. This is a backend-heavy engineering role. You'll spend your time writing MCP tool integrations, building agentic data pipelines, and architecting the infrastructure that lets AI agents act reliably inside our platform. You'll also work across our broader Django stack when the work calls for it.

The ideal candidate is a strong systems engineer who's genuinely curious about agentic AI and wants to build the infrastructure behind it, not just talk about it. You bring deep backend skill, and we'll give you a fast-moving, high-leverage problem to point it at.

Because this is a senior position, you'll operate with real autonomy. You'll make architecture calls, shape scope, and mentor other engineers as the team grows.

• Design and build MCP tool integrations connecting our platform to model providers and external systems
• Architect data pipelines that ingest, structure, and process AI agent outputs at scale
• Build and maintain core platform features in Django and Python, alongside the agentic work
• Make sound tradeoffs between build speed and long-term maintainability, and be able to defend those calls
• Partner with our CTO on technical architecture for the agentic roadmap
• Set technical standards for agentic development that other engineers can follow as the team grows
• Debug and harden systems in production, since agent behavior surfaces failure modes that traditional software doesn't

• 5+ years of professional backend software engineering experience, with real depth in Python
• Strong experience with Django or a comparable web framework, and comfort working across a full stack when needed
• Experience building data pipelines: ingestion, transformation, and reliable processing at scale
• Familiarity with LLM tool-calling patterns, MCP, or similar agent-orchestration concepts, with genuine excitement about going deeper here
• Strong systems thinking. You reason naturally about failure modes, retries, idempotency, and data consistency
• A track record of finding good patterns in ambiguous, fast-moving technical spaces
• Excellent written communication. You can document a design decision clearly enough that someone else can pick it up and run

• Experience with async Python (asyncio, FastAPI, or similar) for streaming or long-running agent workloads
• Prior experience at an early or growth-stage startup
• Exposure to vector databases, embeddings, or RAG architectures
• Experience with AWS infrastructure (RDS, Redis, or similar)
• Base salary: $145,000 - $165,000, commensurate with experience
• Meaningful equity in an early-stage, venture-backed company