Data Scientist- ML Engineering

We are:

impact.

With the right people and the right ideas, there’s no limit to what we can achieve

Are you a fit?

Key Responsibilities

• Architect end-to-end ML infrastructure, including pipelines, model serving, monitoring, and governance.
• Lead deployment of high-impact ML solutions such as forecasting engines, optimization models, and NLP use cases.
• Design and manage advanced CI/CD workflows using Azure Pipelines, MLflow, and Databricks.
• Implement model registry, versioning, lineage, and audit-compliant governance frameworks.
• Build and maintain monitoring systems to detect model drift and automate retraining cycles.
• Mentor MLOps engineers and collaborate with platform, data, and product teams to ensure seamless integration.
• Drive adoption of MLOps best practices across containerization, observability, testing, and scalable infrastructure.

Must-have Skills

• 5–8+ years of experience in ML Engineering, MLOps, or building large-scale ML systems.
• Strong expertise with Spark, Azure Databricks, MLflow, Kubernetes, and Docker.
• Proven track record deploying ML solutions at enterprise scale with audit, governance, and monitoring layers.
• Experience designing ML infrastructure and CI/CD pipelines in cloud environments.
• Knowledge of hybrid or multi-cloud architectures.
• Bachelor’s degree required; Master’s preferred in Computer Science, Engineering, or related fields.

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.

What we offer:

• A High-Impact Environment
• Commitment to Professional Development
• Flexible and Collaborative Culture
• Global Opportunities
• Vibrant Community
• Total Rewards