[Hiring] Agentic Systems Engineer @Kantiv (formerly Joist AI)

Role Description

Kantiv is looking for an engineer with 2–4 years of experience to help build the next generation of agentic applications that streamline proposal writing for the AEC industry. These are systems that reason, use tools, remember, and collaborate with users. The stack spans multi-agent orchestration, MCP servers, skills, long-term memory, evals, retrieval, and the plumbing that makes all of it hold up in production.

What you'll do:

• Build agents as modular, plug-and-play components that slot cleanly into the wider stack.
• Add memory layers (short-term, long-term, summarization, retrieval-backed) into running systems.
• Wire up tool integrations, MCP servers, and skills.
• Own quality of the features you put out: tests, evals, observability, the works.
• Dig into production traces to understand what the system is actually doing, and close the loop with fixes.

Qualifications

• 2–4 years of writing production software.
• Strong Python skills.
• Solid grounding in core agentic and LLM concepts.
• Experience building something non-trivial with the modern agent toolkit.
• Able to drop into an unfamiliar codebase and find your way around fast.
• A keen eye for detail.
• Data-driven by default.
• Hands-on experience with Langfuse or LangSmith (or equivalent tracing/observability for LLM systems).
• Genuine curiosity about the frontier.

Requirements

• Search and retrieval: embeddings, vector databases, hybrid retrieval, rerankers.
• LLM evaluations end-to-end: designing evals, choosing what to measure, building the harness.
• LangGraph depth: building custom graphs, understanding checkpointers, working with context-management nodes.

What to expect

We conduct a rigorous interview process based on integrity, talent, and drive. We trust our teammates from day one and move quickly to evaluate your fit for the role. The entire interview process typically takes two weeks. Here's what to expect:

• A 30 minute Zoom meeting to talk about Kantiv, your background, and answer any questions about the role.
• 45-minute Python proficiency / agentic coding proficiency test. 2 problems: 1 to be coded by hand, the other using Gen AI.
• 60 min project, deep dive into the work they have done. A short presentation followed by a Q&A.
• 45 min interview on Gen AI / LLM fundamentals.
• 30 min culture fit.