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ML Ops Engineer, Model Accuracy
Minor Hotels Europe and AmericasML Ops Engineer at Capgemini designing and building scalable backend platforms for ML workloads. Collaborating with teams to build cost-effective cloud-native infrastructure and ML pipelines.
Tech Stack
Tools & technologiesAirflowAWSAzureCloudDockerGoGoogle Cloud PlatformJavaKubernetesMicroservicesPython
About the role
Key responsibilities & impact- Design and build scalable, reliable backend and platform systems optimized for ML workloads
- Enforce strong engineering practices including modular design, automated testing, code quality, and scalability standards
- Develop and manage cloud-native infrastructure with Kubernetes, containers, and microservices, while optimizing for cost and resilience
- Establish and scale CI/CD pipelines across both application and ML lifecycles, with full observability (logging, metrics, tracing)
- Architect and implement end-to-end MLOps pipelines, from data ingestion through deployment, monitoring, and automated retraining
- Drive automation in model lifecycle management including versioning, experiment tracking, reproducibility, and governance using tools like MLflow, Kubeflow, and Airflow
- Define, monitor, and continuously improve model performance using key metrics (accuracy, latency, drift, bias), with robust evaluation and A/B testing frameworks
- Lead cross-functional teams and collaborate with stakeholders to deliver scalable AI solutions while shaping the ML platform strategy and adoption roadmap
Requirements
What you’ll need- Strong foundation in Software Engineering (Java/Python/Go)
- Expertise in DevOps practices (CI/CD, Docker, Kubernetes, Infrastructure as Code)
- Proven experience in MLOps frameworks and model lifecycle management
- Deep understanding of model accuracy, evaluation metrics, and monitoring strategies
- Hands-on experience with cloud platforms (GCP/AWS/Azure)
- Prior experience managing engineering teams
Benefits
Comp & perks- Flexible work arrangements
- Career growth programs
- Diverse professions to explore
- Support for healthy work-life balance
ATS Keywords
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Hard Skills & Tools
JavaPythonGoDevOpsCI/CDDockerKubernetesMLOpsInfrastructure as Codemodel lifecycle management
Soft Skills
leadershipcollaborationcross-functional teamwork