Applied AI Engineer – Sidekick
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Oct 13, 2026.
Aboutmonday.com:
monday.com is the AI work platform powering the most ambitious teams. 250,000+ customers across departments use us to bring people, workflows, and AI agents together on one flexible platform where AI doesn’t just assist, it executes. We move fast, build things that matter, and foster an ownership-driven culture where you’re empowered to shape how organizations work and outpace their competition.
About the team:
Sidekick ismonday.com’s AI assistant, used by 2M+ people to plan, analyze and get work done across the platform. We’re a small, senior group that turns the latest agentic research into features that run reliably at scale, and we own them end to end: from early prototype to production quality.
About the role:
Design, build and ship production AI agents: tool use, multi-step reasoning, memory and context management
Bring state-of-the-art agentic techniques into the product
Own agent quality: build offline evals (golden datasets, LLM-as-judge, regression suites) and online metrics, and drive improvements you can measure
Balance quality, latency and cost in production, with solid observability and tracing
Work with product and design to turn open-ended goals into shipped AI features
Requirements:
4+ years of software engineering experience, including hands-on work shipping AI agents or LLM-based features to production
Hands-on experience with agent frameworks (e.g., LangGraph, Vercel AI SDK or Claude Agent SDK) and core agentic patterns: tool calling and MCP, structured outputs, context management
Experience evaluating AI agents and LLM-based features, offline and online, to measure and improve quality over time
Comfortable owning ambiguous, open-ended problems end to end, from exploration and prototyping to production, with limited direction
Keeps up with a fast-moving field and adopts new techniques where they make a real difference
Nice to have:
TypeScript/Node.js (the team’s primary stack)
High-scale data infrastructure: Kafka, Cassandra, MySQL, CDC
Full-stack experience (React)
Background in data science or applied ML