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Data Scientist - ML Engineering

Roles & Responsibilities

  • Bachelor's degree required; Master's preferred in CS, Engineering, or related field.
  • 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.

Requirements:

  • Collaborate with clients to leverage data and AI, accelerating market entry and driving business transformation.
  • Lead the design, development, and deployment of ML solutions at enterprise scale, including audit and monitoring mechanisms.
  • Translate technical topics into business impact and communicate clearly and persuasively with clients and senior stakeholders.
  • Guide cross-functional ML/DevOps teams and contribute to leadership initiatives in ML platforms.

Job description

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:

Requirements

  • Bachelor's required; Master's preferred in CS, Engineering, or related.
  • 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.

Soft Skills 

1. Strategic, persuasive, and business-oriented communication.

  1. Strong storytelling to explain, defend, and "sell" complex solutions.
  2. Ability to lead complex conversations with clients and senior stakeholders.
  3. Clear, structured, and confident responses to objections or ambiguous scenarios.
  4. Ability to translate technical topics into business impact and decision-making.
  5. Ability to build trust, credibility, and alignment through communication.

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.

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