About The Role

ML Engineer – Job Description

PURPOSE AND SCOPE

Design, build, and scale machine learning systems that enable advanced analytics, predictive modeling, and data-driven decision-making.
Partner with data science, engineering, and business teams to productionize models and embed them into enterprise applications and workflows.
Ensure robust, reliable, and governed ML solutions aligned with enterprise architecture and responsible AI principles.

PRINCIPAL DUTIES AND RESPONSIBILITIES

Develop, deploy, and maintain end-to-end ML pipelines, including data ingestion, feature engineering, model training, and inference at scale.
Collaborate with data scientists to operationalize models and optimize performance, scalability, and cost in production environments.
Monitor and maintain model performance, implementing retraining, versioning, and continuous improvement processes.

EDUCATION

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics, or related quantitative field.
Strong academic foundation in machine learning, statistics, and software engineering principles.
Relevant certifications in cloud platforms (AWS, Azure) or data engineering/ML engineering preferred.

EXPERIENCE AND REQUIRED SKILLS

Strong experience building and deploying ML solutions on AWS and Azure, including services such as SageMaker, Azure Machine Learning, and cloud-native data pipelines.

Hands-on expertise with Databricks (Spark, Delta Lake) for scalable data processing, feature engineering, and model training in distributed environments.

Proficiency with Azure DevOps and GitHub for source control, CI/CD pipelines, and MLOps practices (model versioning, automated deployment, monitoring).

Experience with ML/AI evaluation frameworks, including defining evaluation metrics, validating model performance (accuracy, drift, bias), and implementing automated evaluation pipelines to ensure model reliability and governance.