Machine Learning Engineer – Data & Analytics (Azure / Databricks)

About the Role:

As part of our Data & Analytics pillar, we are scaling our regional data platform to unlock advanced analytics, machine learning, and AI-driven use cases across multiple markets.

You will contribute to the development and deployment of AI/ML solutions, working closely with cross-functional teams to turn data into actionable insights and scalable products. The position plays a key role in supporting the organization’s journey toward more advanced analytics and data-driven decision-making.

You will work closely with data engineers, BI analysts, and business stakeholders to build, deploy, and scale machine learning solutions on our evolving Azure-based ecosystem, with a strategic transition towards Databricks-based architecture.

Key Responsibilities:

Design, develop, and deploy machine learning models to solve business problems

Work on end-to-end ML lifecycle:

Data exploration and feature engineering

Model development and training

Evaluation, deployment, and monitoring

Collaborate with Data Engineers to leverage data pipelines built on Azure Data Factory and evolving Databricks platform

Contribute to building MLOps capabilities (model versioning, monitoring, CI/CD pipelines)

Support ongoing AI/ML PoCs and help scale successful use cases into production

Partner with business stakeholders to translate analytical needs into scalable ML solutions

Ensure solutions are aligned with enterprise data governance and security standards

Contribute to defining the AI/ML roadmap and best practices within the organization

Required Skills & Experience:

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field

3+ years of experience in Machine Learning / Data Science roles

Strong programming experience in Python

Hands-on experience with ML frameworks (e.g., Scikit-learn, TensorFlow, PyTorch)

Solid understanding of ML algorithms, statistics, and data modeling

Experience working with large datasets and distributed processing concepts

Familiarity with cloud platforms (preferably Microsoft Azure)

Experience with Databricks and Spark-based data processing

Knowledge of ML lifecycle tools

Experience with MLOps and CI/CD practices

Understanding of data lake / lakehouse architectures

Exposure to advanced AI use cases (e.g., NLP, forecasting, recommendation systems, generative AI)

What You Will Bring:

Strong problem-solving mindset with a business-driven approach

Ability to work in a cross-functional, multi-market environment

Curiosity to explore emerging AI technologies and translate them into practical solutions

Ownership and accountability in delivering scalable AI solutions