We are looking for a skilled Machine Learning Engineer to design develop and deploy machine learning models and algorithms. The ideal candidate will have experience in building scalable and efficient machine learning systems a strong understanding of algorithmic principles and the ability to turn complex data into actionable insights. You will work closely with data scientists software engineers and product teams to bring innovative ML solutions to life.
Description:
1.Model Development & Optimization
Design and develop machine learning models tailored to banking needs including credit scoring fraud detection customer segmentation and churn prediction.
Optimize model performance for realtime predictions and recommendations.
Use advanced techniques like deep learning NLP and timeseries analysis as required.
2. Data Preparation & Feature Engineering
Work with largescale banking datasets to perform ETL processes using Azure Data Factory and Azure Data bricks.
Conduct feature engineering data cleaning and transformation to prepare datasets for machine learning.
3. Deployment & Monitoring
Deploy ML models on Azure Machine Learning Service and set up monitoring for realtime and batch processing.
Manage versioning scalability and maintenance of models using Azure DevOps.
4. Collaboration & Stakeholder Engagement
Collaborate with data scientists software engineers and stakeholders to understand requirements and translate them into ML solutions.
Engage with banking teams to align ML models with compliance and regulatory standards.
5. Documentation & Reporting
Document model design assumptions and results for transparency and reproducibility.
Generate reports and present insights to technical and nontechnical stakeholders.
Requirements
Education:
Bachelor s or Master s in Computer Science Data Science Machine Learning or related fields.
Skills:
Strong programming skills in Python and familiarity with ML libraries (e.g. ScikitLearn TensorFlow PYTorch).
Proficiency in SQL and database systems with knowledge of Azure SQL Database or Azure Cosmos DB.
Experience with MLOps containerization (e.g. Docker) and deployment in Azure.
Strong understanding of banking use cases like credit scoring fraud detection and risk management.
Certifications:
Microsoft Certified: Azure AI Engineer Associate or Azure Data Scientist Associate.
Qualifications Education: Bachelor s or Master s in Computer Science, Data Science, Machine Learning, or related fields. Skills: Strong programming skills in Python and familiarity with ML libraries (e.g., Scikit-Learn, TensorFlow, PY-Torch). Proficiency in SQL and database systems, with knowledge of Azure SQL Database or Azure Cosmos DB. Experience with MLOps, containerization (e.g., Docker), and deployment in Azure. Strong understanding of banking use cases like credit scoring, fraud detection, and risk management. Certifications: Microsoft Certified: Azure AI Engineer Associate or Azure Data Scientist Associate.