Machine Learning Operations Specialist

**Job Description**:
The Machine Learning Operations Specialist plays a key technical role within the Data & Analytics team, responsible for building, deploying, and optimizing machine learning models that drive scalable, production-grade AI capabilities. Reporting to the AI Team Lead, the ML Engineer transforms data science prototypes into robust, high-performance solutions by implementing end-to-end pipelines, automating deployment, and operationalizing AI within modern cloud and enterprise platforms. This role is essential to delivering reliable, governed, and reusable AI capabilities across the organization.

The MLOps Specialist will also play a central role in advancing the organization's MLOps and LLMOps maturity, ensuring models are not only performant, but also secure, monitored, and easily maintainable through automation and best practices.

The MLOps Specialist collaborates closely with data scientists, software engineers, and business stakeholders to ensure that machine learning solutions are aligned with business needs and seamlessly integrated into broader systems and workflows.

**What does your typical day look like?**

End-to-End ML Pipeline Implementation & Deployment
- Design and implement robust, reproducible machine learning pipelines from experimentation through deployment.
- Develop scalable workflows for data preparation, model training, evaluation, and prediction using production-grade tools and frameworks.
- Optimize training and inference performance for reliability and efficiency at scale.

MLOps & LLMOps Automation

Automate model deployment using CI/CD pipelines and containerized environments.
- Implement and manage a model registry, monitor drift, performance, and versioning.
- Define rollback strategies and model governance policies for responsible AI deployment.
- Contribute to the development and standardization of GenAI integration workflows and tools.
- Implement real-time model monitoring, performance alerting, and drift detection to ensure reliability and ongoing model accuracy in production.

Platform Integration & Performance Optimization
- Leverage cloud-native tools and platforms to deploy and scale models effectively (e.g., Snowpark, container services, orchestration frameworks, Dataiku).
- Work closely with platform teams to ensure alignment between model requirements and system capabilities.
- Leverage Azure-native tools such as Azure Data Factory to support upstream data flow into ML workflows where needed.
- Continuously optimize data and model pipelines for efficiency, resilience, and cost-effectiveness.

Continuous Learning, Collaboration & Innovation
- Stay ahead of emerging technologies and best practices in AI/ML and GenAI deployment.
- Share knowledge and collaborate with peers, data scientists, and engineers to elevate overall technical capability.
- Participate in sprint planning, retrospectives, and cross-team reviews to align on shared objectives and improve team processes.

**Major Challenges**
- Building ML systems that balance rapid experimentation with long-term maintainability and governance.
- Managing increasing complexity in deployment, monitoring, and scaling of models across platforms.
- Adapting to evolving GenAI trends while maintaining rigorous standards around model evaluation, explainability, and control.
- Ensuring alignment with enterprise security and compliance requirements without slowing down innovation.

**Major Job Accountabilities**
- Translate data science prototypes into robust, scalable, and reusable machine learning pipelines that cover data ingestion, feature engineering, training, evaluation, and inference.
- Ensure solutions are modular, maintainable, and production-grade by following software engineering best practices.
- Develop and manage CI/CD workflows for automated model deployment, testing, and monitoring using containerized and cloud-native infrastructure.
- Operationalize models through proper registration, version control, rollback policies, and real-time monitoring for drift and performance anomalies.
- Deploy and integrate models within enterprise platforms such as Snowflake (Snowpark), Dataiku, and other Azure-native tools while ensuring optimal performance, cost-efficiency, and platform alignment.
- Collaborate with infrastructure and platform teams to ensure system compatibility and performance at scale.
- Implement processes for secure, explainable, and governed model deployment in alignment with regulatory and enterprise risk requirements.
- Maintain visibility over model behavior in production through monitoring dashboards, alerts, and audit logs.
- Contribute to the development and standardization of GenAI workflows and reusable components.
- Share knowledge and tools across teams to promote scalable best practices in ML engineering.
- Participate in agile ceremonies, sprint reviews, and architectural discussions to support cross-team alignment.

**Success Measures**
- Delivery of reliable, high-perfor