Designing and operating cloud - based MLOps capabilities supporting analytical and generative AI models. Collaborating with data science and business teams for high - impact AI solutions.
About the role
- Senior Machine Learning Engineer at Disney designing machine learning models for self-healing infrastructure. Collaborating with cross-functional teams to enhance enterprise technology strategies.
Responsibilities
- Work alongside our first-class applications, infrastructure & operations teams to understand current manual processes and business requirements
- Architect, design, and implement reusable machine learning frameworks, patterns, and services that integrate into the enterprise automation and observability platforms
- Design, train, and deploy machine learning models for anomaly detection, forecasting, predictive analytics, event correlation, pattern recognition, classification, causal analysis, and more in distributed environments that can be used to surface leading indicators of failure
- Build near-real-time inference pipelines that generate actionable insights from live telemetry, including continuous streams of metrics, logs, traces, and operational events
- Create data abstractions and perform feature engineering on high-volume, high-cardinality telemetry data
- Evaluate model performance using real production signals and continuously iterate to improve accuracy and reliability
- Build closed-loop, event-driven systems where model signals trigger automated remediation actions
- Partner with infrastructure and SRE teams to identify opportunities and integrate machine learning and AI-driven insights into operational tools, workflows, and dashboards
- Analyze incident and historical data to uncover leading indicators and predictive signals
- Own the full machine learning lifecycle: experimentation, validation, deployment, monitoring, and retraining
- Breakdown targeted, manual processes into reusable software modules that leverage machine learning models
- Build emulation and simulation environments (digital twins) of the infrastructure to test AI/ML-driven automation under realistic scenarios and allow for faster ideation and iteration for architects and engineers.
- Develop algorithms and frameworks to integrate machine learning and AI technologies into our orchestration platform
- Ensure service reliability, performance, and operational uptime through code-driven solutions.
- Conduct root cause analysis, design fault-tolerant architectures, and enable self-healing automation.
- Implement monitoring dashboards and KPIs to provide visibility into automation and tooling performance.
- Collaborate with cross-functional teams including network engineers, software developers, machine learning engineers, and operations teams across the enterprise.
- Support the integration of commercial and open-source tools while maintaining a vendor-agnostic implementation.
Requirements
- 7+ years of software engineering experience, with expertise in automation, machine learning, and AI technologies
- Proven hands-on experience building production-grade ML models and inference pipelines; strong proficiency with modern ML frameworks such as PyTorch, TensorFlow, Scikit-learn, etc.
- Design, train, and deploy machine learning models for anomaly detection, forecasting, predictive analytics, event correlation, pattern recognition, classification, causal analysis, and more in distributed environments that can be used to surface leading indicators of failure
- Proven hands-on experience using software to build frontend, APIs, and backend functionality; strong proficiency with Python, JavaScript, TypeScript, Go, or Rust
- Strong hands-on experience building and deploying event-driven or streaming data, machine learning models in production
- Solid foundation in statistics, data analysis, and applied machine learning techniques
- Experience working with large-scale, real-world datasets (noisy, incomplete, non-standardized, and evolving)
- Experience operationalizing models in distributed, production environments
- Ability to translate ambiguous operational problems into solvable machine learning use cases
- Experience with modern cloud platforms, container orchestration (Kubernetes/Docker), identity/auth frameworks, data and workflow orchestration.
- Experience with AI/ML technologies and data engineering concepts.
- Preferred: Proven hands-on building AI agents.
- Preferred: Certifications such as Kubernetes (CKA/CKAD), AWS/Azure/GCP certifications, CCNP/DevNet or NVIDIA AI engineer.
- Preferred: Experience developing low-code/no-code automation platforms or reusable developer toolkits.
Benefits
- A bonus and/or long-term incentive units may be provided as part of the compensation package
- Full range of medical, financial, and/or other benefits, dependent on the level and position offered
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