For more than 20 years, our global network of passionate technologists and pioneeringcraftspeople has delivered cutting-edge technology and game-changing consulting tocompanies on the brink of AI-driven digital transformation. Since 2001, we have grown into afull-service digital consulting company with 5500+ professionals working on a worldwideambition. Driven by the desire to make a difference, we keep innovating. Fueling the growth of ourcompany with our knowledge worker culture. When teaming up with Xebia, expect in-depthexpertise based on an authentic, value-led, and high-quality way of working that inspires allwe do. About the Role We’re seeking a skilled and driven MLOps Engineer to join our Data & AI practice at Xebia. In this role, you’ll play a critical part in automating, scaling, and operationalizing machinelearning workflows in the AWS ecosystem. You will work closely with data scientists,engineers, and Dev Ops teams to enable reliable deployment, monitoring, and lifecyclemanagement of ML models across production environments. This role is ideal for anengineer who thrives at the intersection of machine learning and infrastructure automation. What You’ll Do - Design and implement robust MLOps pipelines using AWS Sage Maker, Lambda, Docker, and CDK. - Automate model training, validation, registration, and deployment of workflows acrossenvironments. - Implement batch inference and real-time endpoint deployments with proper scalabilityand governance. - Configure and monitor ML endpoints, tracking performance, data drift, and model health metrics. Infrastructure & CI/CD Automation - Build and manage cloud-native infrastructure with AWS CDK to support ML pipelines,storage, and APIs. - Set up Git Lab CI pipelines for ML workflows, including automated testing, artifactscanning, and vulnerability detection. - Implement deployment strategies (blue-green, canary) for model rollouts andupdates. - Integrate API Gateway, Lambda, and Docker-based services into ML lifecycleworkflows. Model Lifecycle Management - Collaborate with data scientists to convert notebooks into reproducible training andinference pipelines. - Manage the full lifecycle of ML models: training, feature engineering, versioning, anddeployment. - Set up robust monitoring for model drift, feature quality, and prediction accuracy. - Use YAML, metadata tracking tools, and Git-based workflows to standardize anddocument ML processes. What You Bring - 5+ years of experience in engineering roles, with at least 2+ years in MLOps or ML-focused infrastructure - Strong proficiency with AWS services such as Sage Maker, Lambda, API Gateway,CDK, and S3 - Practical experience implementing CI/CD workflows with Git Lab CI, including artifactscanning and security practices - Experience containerizing and deploying ML workloads using Docker and managingconfigurations via YAML - Deep understanding of MLOps concepts including model registration, versioning,endpoint deployment, and monitoring - Familiarity with data science workflows including feature engineering, trainingpipelines, and batch inference - Solid understanding of SDLC, agile delivery, and best practices in software reliabilityand security Nice to have: - Exposure to model monitoring frameworks or tools like Sage Maker Model Monitor,Evidently, or Prometheus - Experience with feature stores, ML metadata tracking (MLflow, Sage Maker Experiments), or data quality tooling - Hands-on experience with IaC for secure API development and model serviceorchestration Apply for this job - indicates a required field First Name * Last Name * Email * Phone * Resume/CV * Enter manually Accepted file types: pdf, doc, docx, txt, rtf #J-18808-Ljbffr