Software Engineer - Agentic Platform
- Compensation
- Not disclosed
- Location
- Dublin, Dublin, Ireland
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 3+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 3+ years building and shipping production software, with a track record of owning systems (not just features) and raising the quality bar for the people around you.
- Strong Python and CS fundamentals, including solid work with databases, queues, or real-time systems. The agent platform runs on Python (FastAPI, Pydantic, async), so you're fluent here or will be ver
- Production agentic / LLM systems, not just calling an API: tool use, retrieval grounding, structured outputs, multi-turn state and continuity, streaming, evals, and designing for non-deterministic beh
- Architectural judgment for AI systems: you keep domain logic decoupled from a fast-moving vendor framework, make build-vs-adopt calls deliberately, and know why that matters when the framework landsca
- Judgment about when to use an LLM and when not to: you reach for deterministic logic, retrieval, or statistics when they're more reliable, cheaper, or more reproducible, and you can tell which is whic
- AI-native workflow: you use AI coding agents (Claude Code, Cursor) as a real part of how you build, prompting for scaffolding, reviewing output critically, and knowing when to push back.
- Strong product sense and judgment. You care about the user experience and about system correctness in equal measure.
- Self-management and a continuous-improvement mindset. We don't over-prescribe how the work gets done.
Nice to have
- Experience with agent frameworks or orchestration: Microsoft Agent Framework, LangGraph, AutoGen, or a runtime you built yourself.
- RAG and tool-use platforms (retrieval over tools and APIs, OpenAPI-driven tool generation, MCP).
- LLM evals and observability: designing them, running them, and acting on the signal, with tracing and cost tooling like Langfuse or OpenTelemetry GenAI.
- Cost engineering on LLM workloads (caching, batching, model routing, prompt compaction).
- Embedding-based retrieval or clustering (vector DBs, hybrid search, HDBSCAN, UMAP, and similar).
- Multi-tenant SaaS architecture: data isolation, per-tenant state, noisy-neighbor concerns.
- Full-stack / core-services depth: production React/TypeScript, and/or our core stack (Elixir/Phoenix with OTP, ClickHouse, Kafka). You won't live here day to day, but it helps where the agent platform
- Time-series anomaly detection or drift monitoring; recommendation or ranking systems with user-feedback loops.
- Spec-driven development, writing or working from specs that drive both human and AI implementation.
- Contributing to developer experience or agentic infrastructure.
- Technical leadership on an engineering team.
- Open source contributions.
Full description
About Userpilot
Userpilot is a leading product analytics and user engagement platform used by product teams at hundreds of companies to understand, segment, and activate their users. The product spans a performant JavaScript SDK that runs inside customers' web apps, a Chrome Extension for building in-app UI without code, and a React dashboard that handles complex real-time data, all backed by a distributed Elixir/Phoenix backend that sustains hundreds of thousands of concurrent WebSocket connections, high-throughput Kafka event ingestion, and real-time content delivery at scale.
We move fast, we ship often, and we believe the best engineers care as much about the product they're enabling as the systems and interfaces they build.
The Role
This is an AI-deep role focused on Lia, Userpilot's agent platform, the system that turns a rich product-data model into reliable, grounded, multi-turn AI experiences. The AI is the product, not just a tool you use to build it.
You'll own and elevate the agent platform: a Python service built on Microsoft Agent Framework, with hybrid retrieval over multiple tool catalogs, complex multi-step orchestration utilizing skills and sub-agents, multi-turn state and grounding, and full trace-level observability and cost accounting, all built on framework-neutral domain contracts.
This is a platform you own and push further, not just keep running. You'll contribute to architecture, raise the reliability and eval bar, and help define where a frontier agentic system goes. We hire engineers who can follow a problem wherever it leads, who know when deterministic logic or statistics beat an LLM and vice versa, and who care about the customer experience as much as the system underneath.
What You'll Work On
- Conversational AI experiences grounded in a rich product-data model, with the tool use, retrieval, streaming, and orchestrated multi-turn grounding required to do it reliably, not just plausibly.
- The agent runtime and orchestration itself: complex, multi-step agentic workflows, behind framework-neutral domain contracts that keep business logic portable.
- Hybrid retrieval and tool grounding: RAG (vector + lexical) over tool catalogs assembled from multiple sources (OpenAPI specs, MCP, …), so the agent calls the right operation with the right arguments against live customer data.
- Packaged AI workflows that produce durable, editable, actionable outputs, not just chat answers that get lost in history.
- The eval, observability, and cost infrastructure that makes all of this safe and economically viable in production: a multi-layer eval harness (deterministic checks plus live, judge-scored reasoning evals), end-to-end tracing, and per-call cost accounting.