AI ML - LLM Specialist

Role Overview: You will be responsible for identifying open-source or licensed models based on use cases and aligning them with available MSIL infrastructure. Additionally, you will proactively assess the need for additional infrastructure and prepare a business case for procurement. Your role will involve designing and executing fine-tuning pipelines using industry standard techniques to adapt models to domain-specific enterprise data. You will focus on tasks such as natural language understanding, code generation, and query augmentation for various programming languages. Furthermore, you will handle data preparation by cleaning, anonymizing, and preprocessing large-scale organizational datasets for training, while incorporating privacy-preserving methods like differential privacy. Developing and maintaining deployment scripts using frameworks like PyTorch and integrating with serving tools for scalable API endpoints will also be part of your responsibilities. In addition, you will prototype LLM integrations with enterprise apps and implement evaluation metrics to measure fine-tuned model performance. Key Responsibilities: - Identify open-source or licensed models based on use cases and align them with available MSIL infrastructure - Assess the need for additional infrastructure and prepare a business case for procurement - Design and execute fine-tuning pipelines using industry standard techniques like LoRA or PEFT - Handle data preparation by cleaning, anonymizing, and preprocessing large-scale organizational datasets - Develop and maintain deployment scripts using frameworks like PyTorch - Prototype LLM integrations with enterprise apps - Implement evaluation metrics to measure fine-tuned model performance - Document processes, create reusable codebases, and mentor future team members as the AI team scales Qualifications Required: - 4+ years of hands-on experience in AI/ML engineering, with at least 2 years focused on LLMs or generative AI - In-depth knowledge of Cloud native technologies and tools - Proven track record of fine-tuning and deploying models in production environments - Experience with enterprise data pipelines and handling sensitive datasets from relational databases - Good understanding of common systems integration methods and technologies - Excellent verbal and communication skills with the ability to work effectively in cross-functional teams (Note: Competencies/Skills section is omitted as it does not provide any additional details relevant to the job description.) Role Overview: You will be responsible for identifying open-source or licensed models based on use cases and aligning them with available MSIL infrastructure. Additionally, you will proactively assess the need for additional infrastructure and prepare a business case for procurement. Your role will involve designing and executing fine-tuning pipelines using industry standard techniques to adapt models to domain-specific enterprise data. You will focus on tasks such as natural language understanding, code generation, and query augmentation for various programming languages. Furthermore, you will handle data preparation by cleaning, anonymizing, and preprocessing large-scale organizational datasets for training, while incorporating privacy-preserving methods like differential privacy. Developing and maintaining deployment scripts using frameworks like PyTorch and integrating with serving tools for scalable API endpoints will also be part of your responsibilities. In addition, you will prototype LLM integrations with enterprise apps and implement evaluation metrics to measure fine-tuned model performance. Key Responsibilities: - Identify open-source or licensed models based on use cases and align them with available MSIL infrastructure - Assess the need for additional infrastructure and prepare a business case for procurement - Design and execute fine-tuning pipelines using industry standard techniques like LoRA or PEFT - Handle data preparation by cleaning, anonymizing, and preprocessing large-scale organizational datasets - Develop and maintain deployment scripts using frameworks like PyTorch - Prototype LLM integrations with enterprise apps - Implement evaluation metrics to measure fine-tuned model performance - Document processes, create reusable codebases, and mentor future team members as the AI team scales Qualifications Required: - 4+ years of hands-on experience in AI/ML engineering, with at least 2 years focused on LLMs or generative AI - In-depth knowledge of Cloud native technologies and tools - Proven track record of fine-tuning and deploying models in production environments - Experience with enterprise data pipelines and handling sensitive datasets from relational databases - Good understanding of common systems integration methods and technologies - Excellent verbal and communication skills with the ability to work effectively in cross-functional teams (Note: Competencies/Skills section is omitted as it