Senior Machine Learning Engineer

Job summary

Build, train, evaluate, and deploy machine learning and deep learning models, including foundation-model fine-tuning and RAG systems, for client projects across industries. Establish and maintain production MLOps workflows, and collaborate with clients and internal teams to scope business problems and deliver scalable, reliable solutions.

Job details

The Role This is a senior, hands-on ML engineering role. You take a business problem, turn it into a machine learning solution, and carry it into production. You’ll work on client projects across different industries, building everything from classical models on structured data to LLM-powered systems in the cloud. The right person has deep expertise in at least one of software, data, or ML and working knowledge of the other two. They can also explain the reasoning behind a design without leaning on tool names. What You Will Do Build, train and ship ML and deep learning models that hold up under real production load Set up and maintain MLOps workflows across cloud environments, including CI/CD, model versioning and performance monitoring Fine-tune foundation models and design RAG systems, from embedding and indexing choices through retrieval strategy and deployment Work directly with clients and internal teams to scope business problems and turn them into ML solutions with clear trade-offs Train, validate and evaluate models on large datasets, and keep them fast, scalable and reliable once they’re live What You Bring 4+ years in machine learning engineering or a closely related role, including production systems you’ve built and deployed Strong Python and hands-on work in PyTorch, TensorFlow or Scikit-learn, covering both tree-based and gradient boosting methods on structured data and deep learning architectures such as CNNs and RNNs Practical LLM experience: fine-tuning, prompt engineering, and RAG pipelines built on vector databases, embeddings and retrieval tuning Working knowledge of MLOps tooling such as MLflow, Kubeflow or Airflow, and experience on AWS, Azure or GCP (multi-cloud is a plus) French is preferred. Consulting or client-facing experience, LangChain or LlamaIndex, Spark and SQL, Docker and Kubernetes, and model governance are all strong assets Why This Role The work spans industries and problem types, so you won’t spend years tuning the same model. In one role you’ll work on classical ML, deep learning and generative AI, and own solutions from the first client conversation to production. The role is hybrid, with three days a week in the office. It comes with competitive pay and room to grow.

What you’ll do

Build, train, evaluate, and deploy machine learning and deep learning models, including foundation-model fine-tuning and RAG systems, for client projects across industries. Establish and maintain production MLOps workflows, and collaborate with clients and internal teams to scope business problems and deliver scalable, reliable solutions.

Requirements

Requires at least four years of machine learning engineering or related experience, including building and deploying production systems, strong Python skills, and practical experience with classical ML, deep learning, and LLM applications. Candidates should have working knowledge of MLOps and a major cloud platform; French is preferred, while consulting experience and tools such as LangChain, Spark, Docker, and Kubernetes are assets.

Listed skills

• Machine learning · Preferred
• prompt engineering · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• Python
• PyTorch
• TensorFlow
• Scikit-Learn
• Deep Learning
• Structured Data Modeling
• Gradient Boosting
• Large Language Models
• Fine-Tuning
• Prompt Engineering
• Retrieval-Augmented Generation
• MLOps
• Cloud Computing
• Model Evaluation
• Client Consulting

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Consulting

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