Senior AI Agent & Workflow Engineer Position ID: 134249 Responsibilities Design Agentic Workflows: Architect end-to-end workflows using Lang Graph (or similar), breaking complex problems into composable steps, states, and policies Problem Decomposition & Solution Design: Translate high-level business problems into structured agentic solutions, defining workflows, components, and trade-offs across multiple design options Build Skills, Tools, and Agents: Implement reusable skills, tools, and agents, and compose them into workflows that are maintainable and testable Choose the Right Building Block: Demonstrate strong judgement on when to build a skill vs. a tool vs. an agent vs. a workflow, based on problem decomposition, system design trade-offs, and long-term maintainability Model Strategy & Integration: Integrate and operate multiple LLM providers, optimizing for latency, cost, and output quality Performance Optimization: Optimize prompt engineering, token usage, chunking strategies, and inference efficiency across the platform Evaluations & Quality: Define and implement evaluation strategies (offline and online), datasets, metrics, and guardrails to validate agent behaviour and measure regression over time Data Structuring & Validation: Design typed schemas (e.G., Pydantic models) for reliable data flows, tool contracts, and structured outputs Observability & Debugging: Implement logging, tracing, and monitoring for agentic systems using tools such as Langfuse or Lang Smith AI‑Accelerated Engineering: Use AI assistants (including Claude Code) in day‑to‑day work for prototyping, refactoring, testing, and documentation—while applying strong engineering judgement Cross‑functional Collaboration: Partner with data scientists, engineers,



and product to translate requirements into robust agentic capabilities and deliver measurable value Work alongside experts in various technological domains Enhance your skills with real‑world experience Contribute to a team that values your input and creativity Qualifications - Must Have 5+ years Python development with strong software engineering fundamentals Hands‑on experience building production systems with Large Language Models High ownership & proactive communication: delivers assigned outcomes autonomously, keeping progress visible and proactively surfacing blockers and trade‑offs Proven experience with Lang Graph or similar agentic frameworks (Lang Chain, other orchestration frameworks acceptable) Strong understanding of evaluation methodologies for LLM/agent quality (datasets, metrics, human review, regression testing) Ability to reason about when to build a skill vs tool vs agent vs workflow, with clear trade‑offs Proven ability to decompose complex, ambiguous business problems into scalable technical solutions, from high‑level requirements to detailed design Qualifications - Nice to Have Comfort using AI assistants (e.G., Claude Code) as part of the daily engineering workflow Familiarity with LLM observability tools (Langfuse, Lang Smith, etc.) Background with vector databases and RAG patterns Understanding of cost optimisation and token accounting in LLM systems Production experience with async Python patterns (asyncio, concurrent request handling) Experience with Azure deployment and cloud‑native architectures Knowledge of evaluation frameworks and metrics for AI output quality What You'll Bring Problem Decomposition & Solution Design: Ability to take ambiguous,



high‑level business problems and systematically break them down into actionable components, defining multiple solution approaches and selecting the most appropriate one based on trade‑offs Clearly defined workflows (agents, tools, skills, orchestration) Technical specifications and contracts for each component Evaluation criteria, success metrics, and test strategy Architectural thinking: Ability to take ill‑defined, complex AI problems and decompose them into clean, modular components Production mindset: Comfortable shipping robust, observable, maintainable AI systems—not prototypes LLM fluency: Deep practical knowledge of how to work effectively with large language models Systems perspective: Understanding of how AI components integrate with APIs, databases, async workers, and monitoring Initiative: Comfortable owning full‑stack design decisions and driving technical direction with the team Benefits Competitive salary and yearly bonus Adaptable working model to support work‑life balance Option to work abroad up to 25 days yearly Over 300 euros to set up your home office and an additional monthly home office allowance Wide range of internal and external trainings, including English, German and Spanish classes Ticket restaurant and Health Insurance with flexibility to exchange it for other benefits Life and accident insurance Collective Life retirement Plan 2000 referral bonus if you bring other talented people to the company Special banking and insurance conditions plus exclusive employee discounts Functional diversity benefits Stock options and mortgage benefits Location Barcelona, Poblenou Equal Opportunity Employer At Zurich, we are an equal opportunity employer. We attract and retain the best‑qualified individuals available, regardless of race/ethnicity, religion, gender, sexual orientation, age, or disability. #J-18808-Ljbffr

📌 Senior Ai Agent & Workflow Engineer (Barcelona)
🏢 Zurich 56 Company
📍 Barcelona