Senior Machine Learning Engineer ( AzureML)

About the Role

The ML Developer will design, build, and maintain machine learning models and data pipelines powering core business use cases. The role is hands-on with Python for model development, feature engineering, and pipeline automation, leveraging Azure ML, and Azure DevOps. Success means robust, production grade models with proven business impact, traceable lineage, and operational excellence at scale.

Key Responsibilities

• Feature Engineering & Model Development

o Translate model prototypes from Data Scientists into Azure ML production pipelines,

including data ingestion, training, inference, and retraining.

o Build and iterate on ML models (forecasting / classification / regression) using modern ML

frameworks (scikit-learn, XGBoost, LightGBM, PyTorch / TensorFlow).

o Develop robust feature pipelines (deterministic code, modular definitions, reusability)

using Pandas / PySpark and orchestrate in AML Pipelines Jobs.

o Design experiments with proper sampling, train-test splits, cross-validation, and metrics

selection (e.g., RMSE, AUC, MAPE).

o Implement model selection, champion / challenger promotion, and versioning strategies.

o Document experiment results for reproducibility and regulatory compliance.

o Productionize models as batch or real-time endpoints via Azure ML.

o Implement model validation gates (drift / shift, prediction distribution checks, champion

vs. challenger results).

o Set up model monitoring dashboards for latency, prediction freshness, data drift, and

feature importance tracking.

o Integrate model deployment / test harnesses with Azure DevOps pipelines for CI / CD.

o Develop FastAPIs to invoke and consume ML models.

o Profile, clean, and transform raw data from Snowflake, SQL, and third-party sources.

o Implement checks for data quality (nulls, schema validation, outlier handling, time

alignment, duplicate detection).

o Automate feature extraction and maintain feature store consistency.

o Work with Product, Data, and QA teams to agree on model acceptance criteria and

experiment reviews.

o Contribute to defect taxonomy (data / model / serving), pipeline observability, and SLO

dashboards.

o Publish model performance reports and SLI / SLO summaries for stakeholders.

Required Qualifications

field.