Resume and JobRESUME AND JOB
IBM logo

AI Engineer

IBM

AI Engineer

IBM logo

IBM

full-time

Posted: December 12, 2025

Number of Vacancies: 1

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

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IBM logo

AI Engineer

IBM

AI Engineer

IBM logo

IBM

full-time

Posted: December 12, 2025

Number of Vacancies: 1

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

Target Your Resume for "AI Engineer" , IBM

Get personalized recommendations to optimize your resume specifically for AI Engineer. Takes only 15 seconds!

AI-powered keyword optimization
Skills matching & gap analysis
Experience alignment suggestions

Check Your ATS Score for "AI Engineer" , IBM

Find out how well your resume matches this job's requirements. Get comprehensive analysis including ATS compatibility, keyword matching, skill gaps, and personalized recommendations.

ATS compatibility check
Keyword optimization analysis
Skill matching & gap identification
Format & readability score

Tags & Categories

Software EngineeringSoftware Engineering

Related Jobs You May Like

No related jobs found at the moment.