At a glance
Automatically prepared from the listing. Check the original description for the full requirements.
Jump to the original descriptionResponsibilities
The founding engineer will own the intelligence layer of the product, including agentic orchestration, LLM systems, and voice infrastructure. They will also be responsible for full-stack development of backend services, data models, and APIs to support the agentic CRM platform.
Requirements
Candidates must have hands-on experience building agentic systems with frameworks like LangGraph and LangChain, along with strong applied LLM engineering skills. Proficiency in full-stack development (Node.js/TypeScript, Python, PostgreSQL) and experience with real-time voice AI are also required.
Working hours
40 hours per week
Work arrangement
Remote OK
Skills
- Agentic Systems
- LangGraph
- LangChain
- LLM Engineering
- Prompt Design
- RAG
- Vector Databases
- Voice AI
- Telephony
- Node.js
- TypeScript
- Python
- PostgreSQL
- State Machines
- Full-stack Engineering
Remote locations (standardized)
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Data & Analytics
- Sales
Keywords
- Agentic Systems
- CRM
- LLM
- LangGraph
- LangChain
- Voice AI
- Full-stack
- Node.js
- TypeScript
- Python
- PostgreSQL
- RAG
- Vector Databases
- State Machines
- Telephony
- OpenAI
- Anthropic
- Claude
- OpenRouter
- Model Context Protocol
- Observability
- Evals
- Data Pipelines
- Revenue Operations
- Startup
- 0-to-1
- Event-driven Architecture
- API
- Latency Tuning
- Prompt Engineering
Original job description
Codexa is building an agent-first CRM. Instead of reps working a CRM by hand, our AI agents run the revenue operation itself: outbound, qualification, and pipeline management, end-to-end, replacing traditional SDR and AE workflows. The MVP is live, we have paying clients, and the core architecture is already running client campaigns.
We're looking to hire a founding engineer to own the intelligence layer of the product. This person lives entirely on the agentic and AI side of Codexa: the orchestration that decides what every agent does next, the LLM systems behind it, and the voice infrastructure that runs live calls. It's an AI-focused seat, but not an AI-only one. We're building a full CRM, so you need real full-stack engineering behind you.
Compensation
This role starts on a fully deferred cash comp rather than a standard salary. It's a market-rate role with pay deferred and repaid at a premium once we hit a funding or revenue milestone. We're upfront that this is a real trade: less cash now, real upside as the company grows. Happy to walk through the specifics directly.
What you'd own
The agentic orchestration layer: the state machine and decision engine that runs each lead end-to-end across email and voice, including our Next Best Action logic, escalation, and handoff rules
Our LLM stack: model integration and routing, prompt systems, tool and function calling, retrieval and vector memory, and the evals and guardrails that keep agent behavior safe and reliable in production
Voice AI: the real-time voice agent stack, from call orchestration and latency tuning to qualification on live calls
The reasoning and data enrichment pipeline as it scales from internal tooling into a productized platform
Application development around all of it: the backend services, data models, and APIs the agents run on
Engineering standards for how we build AI systems from the ground up, where your calls on frameworks and architecture will stick
What we're looking for
Hands-on experience building agentic systems with orchestration frameworks like LangGraph and LangChain, not just calling an LLM in a loop
Strong applied LLM engineering: prompt design, tool and function calling, RAG, vector databases, and structured outputs, with real production experience
Voice AI experience: real-time voice agents, telephony, and the ability to structure and tune latency-sensitive call flows
Comfort integrating and routing across model providers (OpenAI, Anthropic/Claude, OpenRouter) and reasoning about cost, latency, and reliability trade-offs
Solid full-stack engineering: able to ship end-to-end across backend, data, and APIs (Node.js/TypeScript, Python, PostgreSQL, queues and workers)
Experience with multi-agent or event-driven architectures and state machines
Startup or 0-to-1 experience preferred, and comfortable with ambiguity and fast iteration
Based in Canada or the US with overlap in Eastern hours.
Strong pluses
Experience with the Model Context Protocol (MCP) for giving agents controlled access to internal and external systems
LLM observability and evals: tracing, prompt versioning, and offline and online evaluation
Multi-tenant, workspace-isolated architecture at scale
Experience building enrichment or data pipelines that feed agent decisioning
