Machine Learning Engineer – Data Classification & Compliance
IBM
Machine Learning Engineer – Data Classification & Compliance
Job Description
Machine Learning Engineer – Data Classification & Compliance
📋 Job Overview
The Machine Learning Engineer role at IBM focuses on leading data classification initiatives within the financial services sector. The position requires developing and deploying machine learning models to manage sensitive data across various databases, ensuring compliance with stringent regulatory standards. This role offers significant opportunities for learning and career growth within a dynamic and innovative environment.
📍 Location: BANGALORE, IN (Remote/Hybrid)
💼 Career Level: Professional
🎯 Key Responsibilities
- Design, build, train, and deploy machine learning models specifically for automated data classification across diverse data stores (SQL, NoSQL, data lakes)
- Apply a deep understanding of financial data types (e.g., PII, account numbers, transaction data) to tune models for accuracy and relevance within the industry context
- Ensure ML models and classification rules adhere strictly to stringent regulatory standards across multiple industries (e.g., GDPR, CCPA, PCI-DSS, SOX, Basel Accords)
- Utilize modern MLOps practices, deploying and managing machine learning models within containerized environments (Docker) for scalability and reproducibility
- Primarily develop using Python ML frameworks, while leveraging expertise in Java and JavaScript for integration with existing enterprise systems and front-end classification interfaces
- Implement robust data classification methods, leveraging both statistical ML approaches and expert-defined business rules for high-precision results
✅ Required Qualifications
- Proven experience as a Machine Learning Engineer with a strong portfolio of deployed ML solutions, particularly those involving natural language processing or data categorization
- Expert-level proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas) for building sophisticated classification models
- Hands-on experience working with data housed in both relational databases (e.g., PostgreSQL, Oracle, DB2) and non-relational databases (e.g., MongoDB, Cassandra)
- In-depth knowledge of data structures, governance needs, and sensitive information types prevalent in the financial services sector
- Strong understanding of global data privacy and compliance regulations applicable to data management
- Practical experience with Docker and/or Kubernetes for deploying scalable microservices and ML models
⭐ Preferred Qualifications
- Familiarity with a range of data science and analytics platforms, such as MATLAB, Altair RapidMiner, or IBM SPSS Statistics
- Proficiency in Java for backend integration and/or JavaScript for front-end interface development related to data governance platforms
- Familiarity with broader data governance and catalog tools (e.g., Informatica, Alation) and how ML integrates with these platforms
- Relevant certifications in Machine Learning or Data Governance
🛠️ Required Skills
- Machine Learning
- Python
- TensorFlow
- PyTorch
- Scikit-learn
- Pandas
- SQL
- NoSQL
- Data Lakes
- PostgreSQL
- Oracle
- DB2
- MongoDB
- Cassandra
- Docker
- Kubernetes
- Java
- JavaScript
- MATLAB
- Altair RapidMiner
- IBM SPSS Statistics
- Informatica
- Alation
- Natural Language Processing
- Data Categorization
- Financial Data Types
- Regulatory Compliance
- Data Privacy
- Data Governance
- MLOps
- Statistical ML Approaches
- Business Rules
🎁 Benefits & Perks
- Opportunity for learning and career growth
- Encouragement to be courageous and experiment everyday
- Continuous trust and support in an environment where everyone can thrive
- Being part of a growth-minded team, always staying curious and open to feedback
- Collaborative and team-focused approach to drive exceptional outcomes for customers
- Embracing challenges with a can-do attitude and outcome-focused approach
Locations
- BANGALORE, IN, India (Remote)
Salary
Estimated Salary Rangemedium confidence
2,500,000 - 4,200,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine Learningintermediate
- Pythonintermediate
- TensorFlowintermediate
- PyTorchintermediate
- Scikit-learnintermediate
- Pandasintermediate
- SQLintermediate
- NoSQLintermediate
- Data Lakesintermediate
- PostgreSQLintermediate
- Oracleintermediate
- DB2intermediate
- MongoDBintermediate
- Cassandraintermediate
- Dockerintermediate
- Kubernetesintermediate
- Javaintermediate
- JavaScriptintermediate
- MATLABintermediate
- Altair RapidMinerintermediate
- IBM SPSS Statisticsintermediate
- Informaticaintermediate
- Alationintermediate
- Natural Language Processingintermediate
- Data Categorizationintermediate
- Financial Data Typesintermediate
- Regulatory Complianceintermediate
- Data Privacyintermediate
- Data Governanceintermediate
- MLOpsintermediate
- Statistical ML Approachesintermediate
- Business Rulesintermediate
Required Qualifications
- Proven experience as a Machine Learning Engineer with a strong portfolio of deployed ML solutions, particularly those involving natural language processing or data categorization (experience)
- Expert-level proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas) for building sophisticated classification models (experience)
- Hands-on experience working with data housed in both relational databases (e.g., PostgreSQL, Oracle, DB2) and non-relational databases (e.g., MongoDB, Cassandra) (experience)
- In-depth knowledge of data structures, governance needs, and sensitive information types prevalent in the financial services sector (experience)
- Strong understanding of global data privacy and compliance regulations applicable to data management (experience)
- Practical experience with Docker and/or Kubernetes for deploying scalable microservices and ML models (experience)
Preferred Qualifications
- Familiarity with a range of data science and analytics platforms, such as MATLAB, Altair RapidMiner, or IBM SPSS Statistics (experience)
- Proficiency in Java for backend integration and/or JavaScript for front-end interface development related to data governance platforms (experience)
- Familiarity with broader data governance and catalog tools (e.g., Informatica, Alation) and how ML integrates with these platforms (experience)
- Relevant certifications in Machine Learning or Data Governance (experience)
Responsibilities
- Design, build, train, and deploy machine learning models specifically for automated data classification across diverse data stores (SQL, NoSQL, data lakes)
- Apply a deep understanding of financial data types (e.g., PII, account numbers, transaction data) to tune models for accuracy and relevance within the industry context
- Ensure ML models and classification rules adhere strictly to stringent regulatory standards across multiple industries (e.g., GDPR, CCPA, PCI-DSS, SOX, Basel Accords)
- Utilize modern MLOps practices, deploying and managing machine learning models within containerized environments (Docker) for scalability and reproducibility
- Primarily develop using Python ML frameworks, while leveraging expertise in Java and JavaScript for integration with existing enterprise systems and front-end classification interfaces
- Implement robust data classification methods, leveraging both statistical ML approaches and expert-defined business rules for high-precision results
Benefits
- general: Opportunity for learning and career growth
- general: Encouragement to be courageous and experiment everyday
- general: Continuous trust and support in an environment where everyone can thrive
- general: Being part of a growth-minded team, always staying curious and open to feedback
- general: Collaborative and team-focused approach to drive exceptional outcomes for customers
- general: Embracing challenges with a can-do attitude and outcome-focused approach
Tags & Categories
Machine Learning Engineer – Data Classification & Compliance
IBM
Machine Learning Engineer – Data Classification & Compliance
Job Description
Machine Learning Engineer – Data Classification & Compliance
📋 Job Overview
The Machine Learning Engineer role at IBM focuses on leading data classification initiatives within the financial services sector. The position requires developing and deploying machine learning models to manage sensitive data across various databases, ensuring compliance with stringent regulatory standards. This role offers significant opportunities for learning and career growth within a dynamic and innovative environment.
📍 Location: BANGALORE, IN (Remote/Hybrid)
💼 Career Level: Professional
🎯 Key Responsibilities
- Design, build, train, and deploy machine learning models specifically for automated data classification across diverse data stores (SQL, NoSQL, data lakes)
- Apply a deep understanding of financial data types (e.g., PII, account numbers, transaction data) to tune models for accuracy and relevance within the industry context
- Ensure ML models and classification rules adhere strictly to stringent regulatory standards across multiple industries (e.g., GDPR, CCPA, PCI-DSS, SOX, Basel Accords)
- Utilize modern MLOps practices, deploying and managing machine learning models within containerized environments (Docker) for scalability and reproducibility
- Primarily develop using Python ML frameworks, while leveraging expertise in Java and JavaScript for integration with existing enterprise systems and front-end classification interfaces
- Implement robust data classification methods, leveraging both statistical ML approaches and expert-defined business rules for high-precision results
✅ Required Qualifications
- Proven experience as a Machine Learning Engineer with a strong portfolio of deployed ML solutions, particularly those involving natural language processing or data categorization
- Expert-level proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas) for building sophisticated classification models
- Hands-on experience working with data housed in both relational databases (e.g., PostgreSQL, Oracle, DB2) and non-relational databases (e.g., MongoDB, Cassandra)
- In-depth knowledge of data structures, governance needs, and sensitive information types prevalent in the financial services sector
- Strong understanding of global data privacy and compliance regulations applicable to data management
- Practical experience with Docker and/or Kubernetes for deploying scalable microservices and ML models
⭐ Preferred Qualifications
- Familiarity with a range of data science and analytics platforms, such as MATLAB, Altair RapidMiner, or IBM SPSS Statistics
- Proficiency in Java for backend integration and/or JavaScript for front-end interface development related to data governance platforms
- Familiarity with broader data governance and catalog tools (e.g., Informatica, Alation) and how ML integrates with these platforms
- Relevant certifications in Machine Learning or Data Governance
🛠️ Required Skills
- Machine Learning
- Python
- TensorFlow
- PyTorch
- Scikit-learn
- Pandas
- SQL
- NoSQL
- Data Lakes
- PostgreSQL
- Oracle
- DB2
- MongoDB
- Cassandra
- Docker
- Kubernetes
- Java
- JavaScript
- MATLAB
- Altair RapidMiner
- IBM SPSS Statistics
- Informatica
- Alation
- Natural Language Processing
- Data Categorization
- Financial Data Types
- Regulatory Compliance
- Data Privacy
- Data Governance
- MLOps
- Statistical ML Approaches
- Business Rules
🎁 Benefits & Perks
- Opportunity for learning and career growth
- Encouragement to be courageous and experiment everyday
- Continuous trust and support in an environment where everyone can thrive
- Being part of a growth-minded team, always staying curious and open to feedback
- Collaborative and team-focused approach to drive exceptional outcomes for customers
- Embracing challenges with a can-do attitude and outcome-focused approach
Locations
- BANGALORE, IN, India (Remote)
Salary
Estimated Salary Rangemedium confidence
2,500,000 - 4,200,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine Learningintermediate
- Pythonintermediate
- TensorFlowintermediate
- PyTorchintermediate
- Scikit-learnintermediate
- Pandasintermediate
- SQLintermediate
- NoSQLintermediate
- Data Lakesintermediate
- PostgreSQLintermediate
- Oracleintermediate
- DB2intermediate
- MongoDBintermediate
- Cassandraintermediate
- Dockerintermediate
- Kubernetesintermediate
- Javaintermediate
- JavaScriptintermediate
- MATLABintermediate
- Altair RapidMinerintermediate
- IBM SPSS Statisticsintermediate
- Informaticaintermediate
- Alationintermediate
- Natural Language Processingintermediate
- Data Categorizationintermediate
- Financial Data Typesintermediate
- Regulatory Complianceintermediate
- Data Privacyintermediate
- Data Governanceintermediate
- MLOpsintermediate
- Statistical ML Approachesintermediate
- Business Rulesintermediate
Required Qualifications
- Proven experience as a Machine Learning Engineer with a strong portfolio of deployed ML solutions, particularly those involving natural language processing or data categorization (experience)
- Expert-level proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas) for building sophisticated classification models (experience)
- Hands-on experience working with data housed in both relational databases (e.g., PostgreSQL, Oracle, DB2) and non-relational databases (e.g., MongoDB, Cassandra) (experience)
- In-depth knowledge of data structures, governance needs, and sensitive information types prevalent in the financial services sector (experience)
- Strong understanding of global data privacy and compliance regulations applicable to data management (experience)
- Practical experience with Docker and/or Kubernetes for deploying scalable microservices and ML models (experience)
Preferred Qualifications
- Familiarity with a range of data science and analytics platforms, such as MATLAB, Altair RapidMiner, or IBM SPSS Statistics (experience)
- Proficiency in Java for backend integration and/or JavaScript for front-end interface development related to data governance platforms (experience)
- Familiarity with broader data governance and catalog tools (e.g., Informatica, Alation) and how ML integrates with these platforms (experience)
- Relevant certifications in Machine Learning or Data Governance (experience)
Responsibilities
- Design, build, train, and deploy machine learning models specifically for automated data classification across diverse data stores (SQL, NoSQL, data lakes)
- Apply a deep understanding of financial data types (e.g., PII, account numbers, transaction data) to tune models for accuracy and relevance within the industry context
- Ensure ML models and classification rules adhere strictly to stringent regulatory standards across multiple industries (e.g., GDPR, CCPA, PCI-DSS, SOX, Basel Accords)
- Utilize modern MLOps practices, deploying and managing machine learning models within containerized environments (Docker) for scalability and reproducibility
- Primarily develop using Python ML frameworks, while leveraging expertise in Java and JavaScript for integration with existing enterprise systems and front-end classification interfaces
- Implement robust data classification methods, leveraging both statistical ML approaches and expert-defined business rules for high-precision results
Benefits
- general: Opportunity for learning and career growth
- general: Encouragement to be courageous and experiment everyday
- general: Continuous trust and support in an environment where everyone can thrive
- general: Being part of a growth-minded team, always staying curious and open to feedback
- general: Collaborative and team-focused approach to drive exceptional outcomes for customers
- general: Embracing challenges with a can-do attitude and outcome-focused approach
Tags & Categories