Experience:

• 5+ years of experience in MLOps, ML engineering, DevOps, or a related discipline, including hands-on experience deploying and operating ML systems in production at scale.
• Prior experience building internal ML/AI platforms used by multiple teams or mentoring engineers in an MLOps/platform capacity.

Qualification:

• Bachelor's or Master's degree (BTech/MTech/MCA) in Computer Science, Information Technology, Electrical/Electronics Engineering, or a related technical field.

Responsibilities:

• Design and build reusable ML infrastructure that supports multiple teams, projects, and use cases across the organization.
• Standardize AI development workflows, from experimentation through to production deployment.
• Enable self-service model deployment with scalable, production-grade inference endpoints.
• Automate data ingestion and validation pipelines to ensure high-quality, reliable inputs for ML systems.
• Build and maintain training and retraining pipelines, including feature engineering workflows.
• Own model packaging and deployment processes, ensuring consistency, repeatability, and CI/CD readiness.
• Implement and optimize GPU-based inference for high-throughput, low-latency model serving.
• Build systems for data and model drift detection to maintain model performance over time.
• Maintain model registries, model cards, and version control for both models and datasets.
• Implement observability, logging, and tracing across ML pipelines and inference services.
• Incorporate guardrails and governance controls into GenAI application workflows to ensure safe, compliant deployment.
• Collaborate in technical reviews and mentor engineering teams on MLOps best practices and trade-offs.

More skills:

feature stores, MLflow, JFrog, Airflow, CVAT, enterprise version control, CI/CD workflows, GenAI orchestration frameworks, model observability, logging frameworks, telemetry strategies, drift detection, A/B testing, shadow deployment strategies

Other:

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• All employment decisions at Siemens are based on qualifications, merit and business need.
• We're dedicated to equality, and we encourage applications that reflect the diversity of the communities we work in.