DATA & AI - Ops - Engineer
DATA & AI - Ops - Engineer
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Job Description
Job Description:
Job Description 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 35 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. Were committed to fostering an inclusive environment where everyone can thrive.
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Employment Type
Full-Time
