Data & Ai - Ops - Engineer

- Data AI Ops Engineer MLOps Engineer Role Overview We are looking for an experienced Data AI Ops Engineer with expertise in MLOps Data Engineering and ML deployment pipelines The role involves designing automating and optimizing end-to-end ML workflows from data preparation to model training deployment monitoring and lifecycle management across Azure ML AWS SageMaker and Google Vertex AI The ideal candidate will bring strong skills in Python PySpark SQL CI CD and containerization along with hands-on experience in model serving monitoring and optimization Key Responsibilities ML Development Deployment Implement and manage end-to-end ML pipelines using Azure ML Pipelines Kubeflow Pipelines and MLflow Support model development with scikit-learn TensorFlow and PyTorch including training tuning and serialization pickle ONNX TorchScript Deploy models into production using Docker Azure ML AWS SageMaker and Vertex AI with scalable serving frameworks MLOps Automation Develop CI CD pipelines for ML workflows using GitHub Actions MLflow CI CD integrations and container registries Implement continuous training CT continuous integration CI and continuous delivery CD practices for ML systems Automate data ingestion preprocessing and feature pipelines with PySpark and SQL Model Monitoring Optimization Monitor model performance drift and data quality in production environments Implement logging alerting and observability for ML models and pipelines Optimize inference performance with ONNX TorchScript and TensorRT optional Collaboration Governance Partner with Data Scientists Data Engineers and DevOps teams to integrate ML models into business workflows Ensure compliance with data governance security and regulatory policies Contribute to the standardization of MLOps frameworks best practices and reusable components Required Skills Qualifications 7 years of experience in Data AI Engineering with 3-5 years in MLOps Strong programming skills in Python PySpark SQL Expertise in ML frameworks scikit-learn TensorFlow PyTorch Experience with model serialization formats pickle ONNX TorchScript Hands-on with CI CD tools GitHub Actions MLflow CI CD Kubeflow Pipelines Azure ML Pipelines Experience deploying ML models on Azure ML AWS SageMaker and Vertex AI Proficiency in Docker and containerized deployments Preferred Skills Familiarity with Kubernetes for scaling ML workloads Experience with feature stores and monitoring tools Feast WhyLabs Evidently AI Prometheus Grafana Knowledge of data governance and compliance GDPR HIPAA etc Exposure to large-scale distributed systems and real-time inference At DXC Technology we believe strong connections and community are key to our success Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing productivity individual work styles and life circumstances We re committed to fostering an inclusive environment where everyone can thrive Recruitment fraud is a scheme in which fictitious job opportunities are offered to job seekers typically through online services such as false websites or through unsolicited emails claiming to be from the company These emails may request recipients to provide personal information or to make payments as part of their illegitimate recruiting process DXC does not make offers of employment via social media networks and DXC never asks for any money or payments from applicants at any point in the recruitment process nor ask a job seeker to purchase IT or other equipment on our behalf More information on employment scams is available