AI Engineer
IBM
AI Engineer
Job Description
AI Engineer
📋 Job Overview
The AI Engineer at IBM will join the AI/ML Center of Excellence to implement AI capabilities across IBM's Automation products. Key responsibilities include leading data collection for LLM training, integrating LLMs into products, and establishing AI/ML best practices. The role requires collaboration with product teams to drive AI-driven innovation.
📍 Location: Krakow, PL (Remote/Hybrid)
💼 Career Level: Professional
🎯 Key Responsibilities
- Design and implement data collection pipelines for LLM training datasets
- Develop data quality assessment frameworks
- Create annotation guidelines and workflows for domain-specific datasets
- Implement data governance protocols ensuring compliance with privacy regulations and ethical AI principles
- Establish evaluation datasets and benchmarks to measure LLM performance
- Architect solutions to integrate LLMs with IBM's products and ecosystem
- Develop APIs and interfaces for LLM interaction with other software components
- Optimize LLM deployment for various computing environments
- Implement model compression, quantization, and optimization techniques
- Design and implement prompt engineering frameworks
- Establish technical standards and best practices for AI/ML feature implementation
- Create reusable components and design patterns for LLM use cases
- Develop monitoring systems for model performance, drift, and biases
- Research and implement techniques for responsible AI
- Collaborate with product teams to identify AI-driven innovation opportunities
✅ Required Qualifications
- Demonstrated experience with NLP and large language models, including model evaluation and algorithm design
- Strong programming skills in Python
- Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX
- Experience with data processing pipelines and working with large datasets
- Knowledge of MLOps practices and tools for model deployment and monitoring
- Ability to work independently and collaborate across diverse teams
🛠️ Required Skills
- NLP
- Large Language Models
- Transformer architectures
- Model evaluation
- Algorithm design
- Python
- PyTorch
- TensorFlow
- JAX
- Data processing pipelines
- Large datasets
- MLOps
- Model deployment
- Monitoring
- Collaboration
- Data collection
- Data quality assessment
- Annotation guidelines
- Data governance
- Privacy regulations
- Ethical AI
- Evaluation datasets
- Benchmarks
- API development
- Interfaces
- LLM deployment
- Model compression
- Quantization
- Optimization
- Prompt engineering
- Technical standards
- Best practices
- Reusable components
- Design patterns
- Monitoring systems
- Model performance
- Model drift
- Biases
- Responsible AI
- Explainability
- Fairness
- Innovation
🎁 Benefits & Perks
- Opportunity to learn and develop career
- Encouragement to be courageous and experiment
- Continuous trust and support in an inclusive environment
- Growth-minded culture with openness to feedback and learning
- Opportunity to provide feedback to help others grow
- Team-focused approach to drive exceptional outcomes
- Courage to make critical decisions
- Embracing challenges with a can-do attitude
- Outcome-focused approach
- Being part of a responsible technology innovator and force for good
- Equal-opportunity employment
Locations
- Krakow, PL, 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
- NLPintermediate
- Large Language Modelsintermediate
- Transformer architecturesintermediate
- Model evaluationintermediate
- Algorithm designintermediate
- Pythonintermediate
- PyTorchintermediate
- TensorFlowintermediate
- JAXintermediate
- Data processing pipelinesintermediate
- Large datasetsintermediate
- MLOpsintermediate
- Model deploymentintermediate
- Monitoringintermediate
- Collaborationintermediate
- Data collectionintermediate
- Data quality assessmentintermediate
- Annotation guidelinesintermediate
- Data governanceintermediate
- Privacy regulationsintermediate
- Ethical AIintermediate
- Evaluation datasetsintermediate
- Benchmarksintermediate
- API developmentintermediate
- Interfacesintermediate
- LLM deploymentintermediate
- Model compressionintermediate
- Quantizationintermediate
- Optimizationintermediate
- Prompt engineeringintermediate
- Technical standardsintermediate
- Best practicesintermediate
- Reusable componentsintermediate
- Design patternsintermediate
- Monitoring systemsintermediate
- Model performanceintermediate
- Model driftintermediate
- Biasesintermediate
- Responsible AIintermediate
- Explainabilityintermediate
- Fairnessintermediate
- Innovationintermediate
Required Qualifications
- Demonstrated experience with NLP and large language models, including model evaluation and algorithm design (experience)
- Strong programming skills in Python (experience)
- Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX (experience)
- Experience with data processing pipelines and working with large datasets (experience)
- Knowledge of MLOps practices and tools for model deployment and monitoring (experience)
- Ability to work independently and collaborate across diverse teams (experience)
Responsibilities
- Design and implement data collection pipelines for LLM training datasets
- Develop data quality assessment frameworks
- Create annotation guidelines and workflows for domain-specific datasets
- Implement data governance protocols ensuring compliance with privacy regulations and ethical AI principles
- Establish evaluation datasets and benchmarks to measure LLM performance
- Architect solutions to integrate LLMs with IBM's products and ecosystem
- Develop APIs and interfaces for LLM interaction with other software components
- Optimize LLM deployment for various computing environments
- Implement model compression, quantization, and optimization techniques
- Design and implement prompt engineering frameworks
- Establish technical standards and best practices for AI/ML feature implementation
- Create reusable components and design patterns for LLM use cases
- Develop monitoring systems for model performance, drift, and biases
- Research and implement techniques for responsible AI
- Collaborate with product teams to identify AI-driven innovation opportunities
Benefits
- general: Opportunity to learn and develop career
- general: Encouragement to be courageous and experiment
- general: Continuous trust and support in an inclusive environment
- general: Growth-minded culture with openness to feedback and learning
- general: Opportunity to provide feedback to help others grow
- general: Team-focused approach to drive exceptional outcomes
- general: Courage to make critical decisions
- general: Embracing challenges with a can-do attitude
- general: Outcome-focused approach
- general: Being part of a responsible technology innovator and force for good
- general: Equal-opportunity employment
Tags & Categories
AI Engineer
IBM
AI Engineer
Job Description
AI Engineer
📋 Job Overview
The AI Engineer at IBM will join the AI/ML Center of Excellence to implement AI capabilities across IBM's Automation products. Key responsibilities include leading data collection for LLM training, integrating LLMs into products, and establishing AI/ML best practices. The role requires collaboration with product teams to drive AI-driven innovation.
📍 Location: Krakow, PL (Remote/Hybrid)
💼 Career Level: Professional
🎯 Key Responsibilities
- Design and implement data collection pipelines for LLM training datasets
- Develop data quality assessment frameworks
- Create annotation guidelines and workflows for domain-specific datasets
- Implement data governance protocols ensuring compliance with privacy regulations and ethical AI principles
- Establish evaluation datasets and benchmarks to measure LLM performance
- Architect solutions to integrate LLMs with IBM's products and ecosystem
- Develop APIs and interfaces for LLM interaction with other software components
- Optimize LLM deployment for various computing environments
- Implement model compression, quantization, and optimization techniques
- Design and implement prompt engineering frameworks
- Establish technical standards and best practices for AI/ML feature implementation
- Create reusable components and design patterns for LLM use cases
- Develop monitoring systems for model performance, drift, and biases
- Research and implement techniques for responsible AI
- Collaborate with product teams to identify AI-driven innovation opportunities
✅ Required Qualifications
- Demonstrated experience with NLP and large language models, including model evaluation and algorithm design
- Strong programming skills in Python
- Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX
- Experience with data processing pipelines and working with large datasets
- Knowledge of MLOps practices and tools for model deployment and monitoring
- Ability to work independently and collaborate across diverse teams
🛠️ Required Skills
- NLP
- Large Language Models
- Transformer architectures
- Model evaluation
- Algorithm design
- Python
- PyTorch
- TensorFlow
- JAX
- Data processing pipelines
- Large datasets
- MLOps
- Model deployment
- Monitoring
- Collaboration
- Data collection
- Data quality assessment
- Annotation guidelines
- Data governance
- Privacy regulations
- Ethical AI
- Evaluation datasets
- Benchmarks
- API development
- Interfaces
- LLM deployment
- Model compression
- Quantization
- Optimization
- Prompt engineering
- Technical standards
- Best practices
- Reusable components
- Design patterns
- Monitoring systems
- Model performance
- Model drift
- Biases
- Responsible AI
- Explainability
- Fairness
- Innovation
🎁 Benefits & Perks
- Opportunity to learn and develop career
- Encouragement to be courageous and experiment
- Continuous trust and support in an inclusive environment
- Growth-minded culture with openness to feedback and learning
- Opportunity to provide feedback to help others grow
- Team-focused approach to drive exceptional outcomes
- Courage to make critical decisions
- Embracing challenges with a can-do attitude
- Outcome-focused approach
- Being part of a responsible technology innovator and force for good
- Equal-opportunity employment
Locations
- Krakow, PL, 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
- NLPintermediate
- Large Language Modelsintermediate
- Transformer architecturesintermediate
- Model evaluationintermediate
- Algorithm designintermediate
- Pythonintermediate
- PyTorchintermediate
- TensorFlowintermediate
- JAXintermediate
- Data processing pipelinesintermediate
- Large datasetsintermediate
- MLOpsintermediate
- Model deploymentintermediate
- Monitoringintermediate
- Collaborationintermediate
- Data collectionintermediate
- Data quality assessmentintermediate
- Annotation guidelinesintermediate
- Data governanceintermediate
- Privacy regulationsintermediate
- Ethical AIintermediate
- Evaluation datasetsintermediate
- Benchmarksintermediate
- API developmentintermediate
- Interfacesintermediate
- LLM deploymentintermediate
- Model compressionintermediate
- Quantizationintermediate
- Optimizationintermediate
- Prompt engineeringintermediate
- Technical standardsintermediate
- Best practicesintermediate
- Reusable componentsintermediate
- Design patternsintermediate
- Monitoring systemsintermediate
- Model performanceintermediate
- Model driftintermediate
- Biasesintermediate
- Responsible AIintermediate
- Explainabilityintermediate
- Fairnessintermediate
- Innovationintermediate
Required Qualifications
- Demonstrated experience with NLP and large language models, including model evaluation and algorithm design (experience)
- Strong programming skills in Python (experience)
- Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX (experience)
- Experience with data processing pipelines and working with large datasets (experience)
- Knowledge of MLOps practices and tools for model deployment and monitoring (experience)
- Ability to work independently and collaborate across diverse teams (experience)
Responsibilities
- Design and implement data collection pipelines for LLM training datasets
- Develop data quality assessment frameworks
- Create annotation guidelines and workflows for domain-specific datasets
- Implement data governance protocols ensuring compliance with privacy regulations and ethical AI principles
- Establish evaluation datasets and benchmarks to measure LLM performance
- Architect solutions to integrate LLMs with IBM's products and ecosystem
- Develop APIs and interfaces for LLM interaction with other software components
- Optimize LLM deployment for various computing environments
- Implement model compression, quantization, and optimization techniques
- Design and implement prompt engineering frameworks
- Establish technical standards and best practices for AI/ML feature implementation
- Create reusable components and design patterns for LLM use cases
- Develop monitoring systems for model performance, drift, and biases
- Research and implement techniques for responsible AI
- Collaborate with product teams to identify AI-driven innovation opportunities
Benefits
- general: Opportunity to learn and develop career
- general: Encouragement to be courageous and experiment
- general: Continuous trust and support in an inclusive environment
- general: Growth-minded culture with openness to feedback and learning
- general: Opportunity to provide feedback to help others grow
- general: Team-focused approach to drive exceptional outcomes
- general: Courage to make critical decisions
- general: Embracing challenges with a can-do attitude
- general: Outcome-focused approach
- general: Being part of a responsible technology innovator and force for good
- general: Equal-opportunity employment
Tags & Categories