AI Engineer - Model Development & MLOps

About the team

As part of the ESXP AI & Analytics organization, you will join a growing team building AI solutions that have tangible impact across Schneider Electric's business. We design, build, and operate AI models that power new digital capabilities, enhance customer experience, and optimize internal processes.

We are now hiring an AI Engineer who can develop high-quality AI models, industrialize them using state-of-the-art MLOps practices, and ensure robust production performance through monitoring, retraining, and continuous improvement.

Mission

You will be responsible for the full lifecycle of AI models, from data preparation and model design to deployment, observability, and continuous improvement. Your work will contribute to delivering reliable, scalable, secure, and high-impact AI solutions across the company.

Key Responsibilities

1. AI Model Design & Development

• Build and validate machine learning and generative AI models for a variety of business use cases.

• Experiment, evaluate, and compare algorithms and architectures.

• Work with domain experts to understand expected performance and constraints.

2. Industrialization & MLOps

• Implement production-grade workflows for data preparation, model training, deployment, monitoring, and retraining.

• Apply MLOps best practices for reproducibility, traceability, explainability, and governance.

• Package models for serving in cloud or edge environments.

3. Production Operations & Monitoring

• Monitor model and data drift, model performance, SLA, and cost usage.

• Implement model observability tools and alerts.

• Lead model retraining strategies and continuous improvement cycles.

4. Collaboration & Ecosystem Integration

• Partner with data engineers, software teams, and AI platform teams to integrate models in products and applications.

• Contribute reusable components, libraries, and documentation to accelerate adoption across teams.

• Engage with stakeholders to understand needs, communicate results, and support deployment.

5. Quality, Security & DevSecOps

• Follow secure coding, model governance, and DevSecOps standards.

• Ensure CI/CD pipelines, testing frameworks, and infrastructure as code are applied consistently.

Required Skills & Experience

Technical

• 4-7+ years of experience in AI/ML model development or MLOps.

• Strong software engineering skills (Python; testing; debugging; CI/CD).

• Solid understanding of ML techniques (supervised, unsupervised, NLP, GenAI, time series).

• Experience with MLOps tools such as MLflow, Databricks, model registries, feature stores.

• Experience deploying AI solutions to cloud environments (Azure, Databricks).

• Knowledge of containers, Kubernetes, Terraform, and cloud security best practices.

Data Engineering Basics

• Ability to build or adapt data pipelines and work with structured and unstructured data.

Soft Skills

• Strong analytical mindset and scientific rigor.

• Ability to work autonomously while collaborating in cross-functional teams.

• Comfortable communicating technical results to non-technical stakeholders.