• Design and build agentic systems: single agents, multi-agent orchestration, and sub-agent patterns for complex workflows (planner/executor, supervisor/worker, hierarchical task decomposition) • Develop MCP (Model Context Protocol) servers and tools that expose internal systems, datasets, and actions to LLM-powered applications in a safe, governed, and reusable way • Implement retrieval systems (RAG, hybrid search, graph-based retrieval) including chunking, embedding, re-ranking, and context-assembly strategies • Build and maintain MLOps automation: CI/CD for models and agents, environment management, artifact handling, and versioning of prompts, models, data, and code • Implement observability for AI systems: tracing, token/latency/cost metrics, quality and drift monitoring, alerting, and incident response • Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Expose well-modelled, governed datasets and APIs that agents, tools, and downstream consumers can rely on • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes

• Design and build agentic systems: single agents, multi-agent orchestration, and sub-agent patterns for complex workflows (planner/executor, supervisor/worker, hierarchical task decomposition) • Develop MCP (Model Context Protocol) servers and tools that expose internal systems, datasets, and actions to LLM-powered applications in a safe, governed, and reusable way • Implement retrieval systems (RAG, hybrid search, graph-based retrieval) including chunking, embedding, re-ranking, and context-assembly strategies • Build and maintain MLOps automation: CI/CD for models and agents, environment management, artifact handling, and versioning of prompts, models, data, and code • Implement observability for AI systems: tracing, token/latency/cost metrics, quality and drift monitoring, alerting, and incident response • Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Expose well-modelled, governed datasets and APIs that agents, tools, and downstream consumers can rely on • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes

• Play a critical role in bridging data science and software engineering • Deliver robust, scalable AI solutions • Ensure seamless integration of AI models into production systems

Role Description We are looking for an Agentic Workflow Backend Engineer to architect and build a platform enabling the creation, orchestration, and execution of AI-driven automation workflows. In this role, you will design backend systems that power agent-based applications, leveraging: - MCP Clients and Servers as interfaces - agentgateway for AuthN - LangGraph for agent orchestration and stateful workflows - LangFuse for observability - AWS infrastructure for scale, reliability, and security You will work closely with data scientists, machine learning engineers, product managers, and DevOps to build a reusable, extensible platform that enables teams to compose intelligent agents, connect them to tools and data sources, and run them safely in production. This position requires strong architectural thinking, hands-on backend development, and comfort owning complex systems end-to-end in a fast-moving, experimental AI environment. Qualifications - 5+ years of experience in Python backend engineering - 2+ years of experience in Agentic workflow implementation and other automations - Proven experience building cloud-hosted, production-grade backend systems - Experience designing or working with distributed systems and microservices - Comfortable working in Agile, fast-paced environments with evolving requirements Requirements - Deep experience in Python with FastAPI (or equivalent modern backend frameworks) - Experience designing microservice APIs and backend service contracts - Experience with LangGraph, LangChain, and orchestration of agent-based AI systems - Proficient with LLM integration patterns, tool calling, and prompt workflows - Solid knowledge of AWS (compute, storage, networking, IAM) - Experience running services on Kubernetes - Strong understanding of databases (PostgreSQL, MySQL, MongoDB or similar) - Familiarity with CI/CD pipelines and Git-based workflows (GitLab preferred) - Excellent debugging, troubleshooting, and system-level thinking skills Education - Bachelor’s degree or higher in Computer Science or a related field, or equivalent practical experience Languages - Excellent written and conversational English (C1 minimum) - French and Spanish are a plus Tools & Technologies - Backend framework: FastAPI - Agent orchestration: LangGraph - Cloud provider: AWS - Container orchestration: Kubernetes - Version control & CI/CD: GitLab - IDE: Cursor Benefits - Impactful work: Build a core platform powering next-generation, AI-driven automation at scale - Professional growth: Work at the frontier of agentic systems with continuous learning opportunities - Competitive compensation: Attractive salary and benefits package - Collaborative environment: Supportive, remote-friendly team with opportunities for travel, training, and industry events