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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Pennylane - **Location:** Paris, France (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Common ISDN Application Programming Interface (CAPI), Python (Programming Language), Machine Learning, Regression Analysis, Role-Based Access Control, Ruby, Datadog, Large Language Models, AI Platforms, Kubernetes, Optimization Algorithms, Deployment Automation, Machine Learning Operations - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/senior-ai-engineer-pennylane-sas-8351873 ## About the Role Are you looking to have an impact on the daily life of millions of entrepreneurs in France (and tomorrow in Europe)? Are you looking for a work environment that values trust, proactivity, and autonomy? Are our Engineering principles aligned with your vision? Then Pennylane is the right place for you!, * Have 5-8 years of experience and are very strong in Python * Hands-on experience building LLM and agentic systems at scale in production: prompting, tool use, context construction, RAG, and handling failure, state and reliability (not just calling a model API). * Treat evaluation as a first-class discipline: golden datasets, LLM-as-judge, human eval, A/B testing, and measuring agent quality, regressions and edge cases. * Have a good grasp of applied LLM / ML and AI infrastructure (model serving, vector databases, cost and latency). * Nice to have: model fine-tuning and post-training (SFT, DPO, RL), MCP, and familiarity with Ruby. * Have a balanced blend of technical, business and product skills, communicate well (including with non-technical domain experts), and are fluent in English (French is not mandatory). ## Description As an AI Engineer, you will be part of one of our ML & AI teams (25+ people, growing from 2 to 5 teams by the end of 2026), depending on your profile and our needs: * AI Capabilities & Infrastructure (CAPI) : the shared foundations every squad builds on: agent harness, agentic runtime & sandboxing, model gateway, tool registry & MCP, guardrails, observability, LLM FinOps, and reusable AI capabilities. * Agent Behaviour : making our agents behave correctly, safely and measurably: behaviour steering, domain tuning, evaluation methodology & harness, and later model fine-tuning. * Studio Assistant : the financial orchestrator agent for accountants and SMEs: understanding intent, picking tools, launching jobs, producing reports, asking for validation when needed. Whichever team you join, the core of the job is the same: * You will build and harden agentic loops in production : LLM call orchestration, tool calling, streaming & events, error recovery, checkpointing and resumability. With security, permissions and cost in mind (agentic RBAC, audit trail, isolated execution, token and latency budgets). * You will own context & memory engineering: context construction, retrieval, compaction, short- and long-term memory, and the latency / cost trade-offs that come with them. * You will make Pennylane usable by agents, turning business capabilities into well-specified, documented, versioned and tested tools exposed internally and through MCP, and steering agent behaviour (system prompts and instructions, skills, planning and tool-selection strategies, guardrails, anti-prompt-injection). * You will treat evaluation as a first-class discipline: golden datasets, LLM-as-judge, human eval, regression tracking and error analysis. Turning production failures into systematic improvements. * You will collaborate with Product teams and domain experts (accountants) on live use cases : ComptAssistant, Studio Assistant, MCP, document extraction, Autopilot bookkeeping, revision. To make sure that fulfilling real user needs is always at the center of what we do. * Stay Ahead of the Curve: Continuously monitor the AI landscape for emerging trends, State-of-the-Art (SOTA) models, and breakthrough research (e.g., new LLM architectures, multimodal AI, and optimization techniques). WHAT you can expect from your life at Pennylane Within one month: * You will learn everything about our company, our teams, and our vision during the first onboarding week. * You will get familiar with our stack and AI tooling (agent harness, tool registry & MCP, evaluation and observability tooling, model gateway), and have delivered a few small projects which will give you a concrete taste of our tools & processes. * You will be given time to meet your future stakeholders, and gain a deep knowledge of our product and operations. Within 3 months: * You will be fully in charge of items in our roadmap, defining and prioritizing your tasks autonomously, and will own an agentic use case end to end in production, together with its evaluation set and quality metrics. * You will be comfortable with our technical stack (Python, agentic frameworks & MCP, evaluation and observability tooling, Kubernetes & AWS). * You will contribute to larger cross-team projects. Within 6 months: * You will proactively contribute to the team's roadmap. * You will work with engineers and data practitioners on improving our agent harness, our evaluation practices and our AI platform. * You will share your learnings and best practices within the team. And beyond: the ML & AI teams will continue growing with the company Which means: * Opportunities to recruit and mentor new team members, * Increased accountability in project leadership, * Responsibilities to design and implement new processes, tools and best practices to make sure that your team works even more efficiently. ## Related Videos - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [Coffee with Developers: David Heinemeier Hansson](https://www.wearedevelopers.com/videos/875-coffee-with-developers-david-heinemeier-hansson) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)