Senior Machine Learning Engineer

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Capgemini

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1967

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Capgemini is a global leader in partnering with businesses to transform and manage their operations by harnessing the power of technology. With expertise across a wide array of industries such as aerospace, automotive, banking, and healthcare, Capgemini provides a constantly evolving portfolio of services to meet the ever-changing needs of their clients. Their offerings include cloud, cybersecurity, data and artificial intelligence, and enterprise management, among others. Capgemini also emphasizes innovation and sustainability, helping companies achieve digital transformation while promoting environmental and social responsibility. Additionally, Capgemini provides career opportunities across various levels and professions, encouraging innovation and diversity in its workforce.

📋 Description

• Architect, deploy, and scale enterprise AI/ML solutions across the machine learning lifecycle • Provide technical leadership, architectural guidance, and engineering best practices • Work with business stakeholders, data scientists, and engineering teams to translate requirements into scalable AI/ML and data engineering solutions • Lead technical discussions, solution design workshops, and architectural reviews • Design, build, and maintain secure, production-ready ML platforms and infrastructure across cloud environments • Develop reusable frameworks, templates, and best practices for model development, deployment, and operationalization • Optimize and refactor large-scale PySpark applications • Configure, tune, and manage Spark clusters for performance, scalability, reliability, and cost efficiency • Design and maintain CI/CD pipelines for model training, testing, deployment, and monitoring • Establish and enforce MLOps practices including version control, experiment tracking, model registry, governance, and reproducibility • Develop and deploy supervised machine learning models for Price Recommendation and Billing Recommendation systems • Balance architecture, hands-on development, operational support, and strategic planning

🎯 Requirements

• Master’s degree in computer science, data science, data engineering, or a related field • 6–10 years of experience in Data Engineering or MLOps • Strong hands-on experience with PySpark optimization and cluster performance tuning • Experience with Azure Databricks, Apache Spark, Azure Machine Learning, and Azure DevOps • Proficiency in Python, SQL, and CI/CD tools • Experience with Agile Software Development • Proven experience developing and deploying supervised machine learning models • Experience building and managing production-grade ML pipelines and enterprise AI platforms • Strong client-facing communication skills and experience gathering requirements, managing stakeholder expectations, and delivering technical solutions • Ability to balance architecture, hands-on development, operational support, and strategic planning