Job Description

The AI Engineer (MLOps), Senior Specialist is an individual contributor role responsible for engineering, deploying, and operating production-grade AI and machine learning solutions at scale across DKSH. The role focuses on MLOps engineering, model lifecycle management, and platform reliability, ensuring that AI models built by data scientists and AI specialists can be securely deployed, monitored, scaled, and continuously improved in production environments.

This role sits at the intersection of AI, software engineering, and cloud platforms. It enables faster time-to-value from AI investments by establishing robust MLOps practices, automation, and standards that support revenue-generating and mission-critical AI use cases.

General responsibilities

• Design, build, and maintain MLOps pipelines for training, testing, deploying, and monitoring AI/ML models in production.
• Engineer scalable and reliable model deployment architectures across cloud environments (Azure), supporting batch and real-time inference.
• Implement automated workflows for model versioning, CI/CD, rollback, and lifecycle management.
• Ensure production AI systems meet requirements for performance, reliability, security, and cost efficiency.
• Monitor models for data drift, performance degradation, and operational issues, and implement remediation strategies.
• Partner closely with AI Specialists, data scientists, and data engineers to productionize models and analytics solutions.
• Develop reusable components, frameworks, and templates to accelerate AI delivery across markets and use cases.
• Integrate AI models into enterprise systems, digital products, and business workflows via APIs and services.
• Support deployment of generative AI and LLM-based solutions with appropriate guardrails, observability, and controls.
• Define and enforce MLOps standards, best practices, and reference architectures for DKSH.
• Improve reliability, scalability, and speed of AI delivery through automation and engineering discipline.
• Contribute to documentation, runbooks, and knowledge sharing to uplift AI engineering maturity across teams.

Job requirements

• Strong experience in MLOps, AI engineering, or machine learning platform roles.
• Proficiency in Python and software engineering best practices.
• Hands-on experience with model deployment, CI/CD, and automation for AI/ML workloads.
• Experience with cloud platforms, preferably Microsoft Azure (e.g. Azure ML, DevOps, containers).
• Strong understanding of machine learning lifecycle concepts, including training, inference, monitoring, and retraining.
• Ability to engineer reliable, secure, and scalable systems in complex enterprise environments.

Preferred skills

• Experience with containerization and orchestration tools (e.g. Docker, Kubernetes).
• Experience with Databricks and MLFlow
• Familiarity with infrastructure-as-code and platform automation.
• Exposure to generative AI and LLM deployment patterns.
• Experience working in agile or product-oriented delivery teams.
• Strong problem-solving mindset and attention to operational detail.