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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - M/W - **Company:** ManoMano - **Location:** Paris, France (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Computer Programming, Information Extraction, Information Retrieval, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Recommender Systems, Data Processing, Enterprise Software Applications, Large Language Models, Snowflake, Apache Spark, Deep Learning, Generative AI, Gitlab, Kubernetes, Production Code, Dask, Machine Learning Operations, Software Coding - **Published:** September 18, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7cdcca3c17816bcf ## About the Role * User-focused mindset with strong analytical skills and a result-oriented approach. * More than 5 years of experience in Machine Learning, Deep Learning, or AI Engineering, including taking models from prototype to production at scale. * Hands-on experience developing and evaluating machine learning or AI solutions for real-world data, with strong experience in model experimentation, evaluation, and benchmarking. Experience with LLMs or VLMs fine tuning is a plus. * Strong experience with at least some of the following: product categorization, taxonomy design, attribute extraction, entity resolution, product matching, semantic similarity, embeddings, or information retrieval. * Experience designing robust data and machine learning pipelines for large-scale production use cases. * Strong scientific rigor and ability to design metrics aligned with catalog quality and product goals, run experiments, analyze errors, and communicate results to guide technical and product decisions. * Experience with large-scale applications in production (monitoring, reliability, performance, observability). * Strong coding skills in Python and proficiency in SQL. You care about code simplicity and performance. * Proficient oral and written communication skills in English. * Growth mindset: always striving to improve your technical and soft skills., * Experience in e-commerce or B2C marketplace environments. * Familiarity with experimentation tools and MLOps practices. * Experience with scalable processing frameworks (Dask, Ray, Spark, etc.). * Some familiarity with Bayesian inference and causal inference. * Knowledge of recommendation systems and personalization. ## Description * Design and implement machine learning and AI solutions for catalog quality, including product categorization, qualification, attribute extraction, product matching, and enrichment. * Build and maintain scalable pipelines for product classification, entity matching, semantic similarity, and attribute extraction across a large and continuously evolving product catalog. * Develop, adapt, and evaluate machine learning models, including LLMs and vision-language models, for domain-specific catalog tasks such as categorization, attribute extraction, product matching, and content enrichment. * Design pragmatic human-in-the-loop and automated workflows that combine machine learning, AI models, rules, and internal data sources to improve catalog quality. * Write production-ready code and deploy AI systems in a live environment at scale. * Define and track evaluation metrics for catalog quality and model performance; create reliable offline benchmarks, run experiments, and communicate results clearly to guide technical and product decisions. * Partner with software engineers, product managers, and business stakeholders to frame problems from both a scientific and business perspective. * Investigate and fix production issues; ensure reliability, observability, and performance of AI systems. * Stay actively engaged in technology watch on the latest developments in machine learning, Generative AI, information extraction, entity resolution, and scalable ML systems. Technical stack: * Python * AWS * Airflow * Kubernetes * Gitlab * Snowflake * Vector databases (e.g. PGVector) * LLM inference & fine tuning frameworks (OpenAI SDK, vLLM, unsloth, or similar), * Introductory call with a talent acquisition manager to get a feel for your motivations and talk about the role. * A take-home technical assignment to assess your machine learning, data processing, and programming skills (3 to 4 hours for an experienced Senior Machine Learning Engineer). * On-site or virtual technical interview with a Senior and a Lead ML/AI Engineer (2h): discuss your take-home assignment and go over internal AI use cases; we assess your critical thinking, knowledge of AI systems, and pragmatism. * A final meeting with a Lead Data Scientist (30 to 45 min). ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [GitLab CI pipelines for a whole company](https://www.wearedevelopers.com/videos/143-gitlab-ci-pipelines-for-a-whole-company) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)