We are:
Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.
With the right people and the right ideas, there’s no limit to what we can achieve
Are you a fit?
Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:
Key Responsibilities
- Architect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance.
- Lead deployment of high-impact models (forecasting engines, optimization solvers, NLP models).
- Design advanced CI/CD workflows using Azure Pipelines, MLflow, and Databricks.
- Implement model registry, versioning, lineage, and audit compliance.
- Build monitoring systems for model drift and retraining automation.
- Mentor MLOps engineers and guide cross-functional platform integration.
- Drive adoption of MLOps best practices, from containerization to observability.
Must-have Skills
- 5–8+ years in ML Engineering, MLOps, or high-scale ML systems.
- Deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, and Docker.
- Proven track record deploying ML at enterprise scale with audit and monitoring layers.
- Familiarity with hybrid/multi-cloud infrastructure.
Nice-to-have:
- AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
- Leadership experience in ML platform or DevOps teams.
- Experience with feature stores and feature engineering. AutoML is a plus, H2O is a plus.
What we offer:
- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
*Specific benefits are determined by the employment type and location.
Find out more about our culture here.
Skills Required
- 5-8+ years in ML Engineering, MLOps, or high-scale ML systems
- Deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, and Docker
- Proven track record deploying ML at enterprise scale with audit and monitoring layers
- Familiarity with hybrid/multi-cloud infrastructure
What We Do
Wizeline, a global technology services provider, builds the best digital products and platforms at scale. We focus on measurable outcomes, partnering with our customers to modernize core technologies, mature data-driven capabilities, and improve user experience.
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