Machine Learning Developer

Job Description

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.

Role & responsibilities

Feature Engineering & Model Development

– Translate model prototypes from Data Scientists into Azure ML production pipelines, including data ingestion, training, inference, and retraining.

– Build and iterate on ML models (forecasting/classification/regression) using modern ML frameworks (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow).

– Develop robust feature pipelines (deterministic code, modular definitions, reusability)using Pandas/PySpark and orchestrate in AML Pipelines Jobs.

– Design experiments with proper sampling, train-test splits, cross-validation, and metrics selection (e.g., RMSE, AUC, MAPE).

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

– Document experiment results for reproducibility and regulatory compliance.

Model Operationalization & Monitoring

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

– Implement model validation gates (drift/shift, prediction distribution checks, champion vs. challenger results).

– Set up model monitoring dashboards for latency, prediction freshness, data drift, and feature importance tracking.

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

– Develop FastAPIs to invoke and consume ML models.

Data Engineering & Quality

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

– Implement checks for data quality (nulls, schema validation, outlier handling, time alignment, duplicate detection).

– Automate feature extraction and maintain feature store consistency.

Collaboration & Quality Ops

– Work with Product, Data, and QA teams to agree on model acceptance criteria and experiment reviews.

– Contribute to defect taxonomy (data/model/serving), pipeline observability, and SLO dashboards.

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

Preferred candidate profile

– 5+ years developing data-focused solutions (3+ years in ML modeling and operations).

– Advanced proficiency in Python (pandas, NumPy, ML frameworks), SQL, and cloud data tools.

– Solid experience building production ML pipelines (Azure ML, Databricks, or equivalent).

– Understanding of model validation, drift detection, and online monitoring.

– Experience with feature stores, CI/CD (Azure DevOps), and API development (FastAPI/Flask).
Role: 

Data Science & Machine Learning – OtherIndustry Type: 

IT Services & ConsultingDepartment: 

Data Science & AnalyticsEmployment Type: 

Full Time, PermanentRole Category: 

Data Science & Machine Learning

EducationUG: 

B.Tech/B.E. in Electronics/Telecommunication, Computer Science and Business System, Mechanical, Artificial Intelligence And Machine Learning, Computer Science Engineering, Electronics And Communication, AIML, Data Science, Electronics And Communication Engineering, Information Science, Information Technology, Artificial Intelligence, Computer Science, Electronic And Communication Engineering, Computer Engineering, Artificial Intelligence And Data Science, Cyber Security, Electronics And Computer Engineering, Computers, Electronics And Instrumentation Engineering, Electronics Engineering

Key Skills

Skills highlighted with ‘‘ are preferred keyskills

Python FrameworkAzure Machine LearningAzure Devops

PysparkML OperationsData EngineeringCi Cd PipelineML FrameworksAPI DevelopmentNumpyData BricksSQLSchema ValidationDuplicate detectionDatafactoryPyTorchPandasSnowflakeAzure DatabaseML ModellingAPI