Type

Full-time

Experience

7+ yr

About this role

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.