MLOps Engineer

As a Data Engineer / MLOps Engineer at Inetum's FabLab, you will play a crucial role in driving the development and implementation of machine learning models and data-driven solutions. Collaborating closely with cross-functional teams, you will leverage your expertise in mathematical techniques and ML frameworks to deliver innovative solutions that address complex business challenges.

Responsibilities:

• Collaborate with stakeholders to understand business requirements and translate them into mathematical and ML-based solutions.
• Design, develop, and implement machine learning models and algorithms for research projects, internal products, and client projects.
• Conduct exploratory data analysis, feature engineering, and model validation to ensure the robustness and accuracy of ML solutions.
• Work closely with software engineers to integrate ML models into production systems and deploy them at scale.
• Stay abreast of the latest advancements in ML techniques and frameworks, and proactively apply them to enhance model performance and efficiency.
• Participate in research initiatives to explore emerging technologies and methodologies, and contribute to the development of innovative solutions within the FabLab.

Requirements

• Master's degree or Ph.D in Computer Science, Engineering, Mathematics, or related field.
• 3-5 years of hands-on experience in data science, machine learning, or related roles.
• Strong proficiency in mathematical techniques such as probability, statistics, and linear algebra.
• Expertise in Python programming and experience with ML libraries/frameworks (e.g., TensorFlow, scikit-learn, PyTorch).
• Solid understanding of data processing techniques and frameworks (e.g., pandas, Spark).
• Experience working on research projects and industry applications, with a proven track record of delivering high-quality ML solutions.
• Excellent communication skills with the ability to collaborate effectively with cross-functional teams and explain technical concepts to non-technical stakeholders.

Preferred Qualifications:

• Experience with deep learning techniques and frameworks (e.g., CNNs, LSTMs).
• Knowledge of cloud platforms and services (e.g., Azure, AWS, Google Cloud).
• Familiarity with big data technologies and distributed computing frameworks.
• Ability to work independently and drive projects from conception to completion.
• Strong problem-solving skills and a passion for exploring innovative solutions to complex problems.