Senior ML Engineer

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

Bachelors or Masters 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.