Note: The job is a remote job and is open to candidates in USA. Capgemini is a global business and technology transformation partner providing engineering, AI, cloud, and data solutions to organizations worldwide. The Senior Machine Learning Engineer will design, deploy, and scale secure enterprise ML platforms, lead technical and architectural discussions, optimize PySpark and Spark workloads, and establish production-grade MLOps practices and pipelines.
Responsibilities
- Build Associate closely working with business stakeholders, data scientists, and engineering teams to understand business requirements and translate them into scalable AI/ML and data engineering solutions
- Lead technical discussions, solution design workshops, and architectural reviews to define end-to-end ML and MLOps implementation strategies
- Design, build, and maintain scalable, secure, and production-ready ML platforms and infrastructure across cloud environments
- Develop reusable frameworks, templates, and best practices to accelerate model development, deployment, and operationalization
- Optimize and refactor large-scale PySpark applications to improve performance, scalability, reliability, and cost efficiency
- Configure, tune, and manage Spark clusters, including executor sizing, resource allocation, partitioning strategies, caching, and workload optimization
- Design, implement, and maintain CI/CD pipelines for automated model training, testing, deployment, and monitoring
- Establish and enforce MLOps best practices, including version control, experiment tracking, model registry, governance, and reproducibility
Skills
- Master's 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 in developing and deploying Supervised Machine Learning models, candidate will be working on Price Recommendation and Billing Recommendation systems
- Experience building and managing production-grade ML pipelines and enterprise AI platforms
- Strong client-facing communication skills with experience gathering requirements, managing stakeholder expectations, and delivering technical solutions
- Ability to balance architecture, hands-on development, operational support, and strategic planning
Benefits
- Remote work arrangement
Company Overview
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