Lead AI Developer and DevOps

Role Overview: You will lead the collaboration with ML Engineers and DevOps Engineers to formulate AI designs that can be built, tested, and deployed through the Route to Live and into Production using continuous integration / deployment. Key Responsibilities: - Model fine-tuning using open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow to improve model performance - Large Language Model Operations (LLMOps) including deploying and managing LLMs on cloud platforms, training, and refining LLMs for specific tasks, scaling LLMs up and down, blue/green deployments, and roll back bad releases - Curating and preparing training data, monitoring and maintaining data quality, transforming, aggregating, and de-duplicating data, making data visible and shareable across data teams, building vector databases, and monitoring LLM performance - Continuous integration and delivery (CI/CD) to automate the model development process, manage infrastructure for distributed model training, and deploy ML models using containerization (Docker) - Utilizing programming languages and frameworks such as Python, LLM pipelines, LangChain, LlamaIndex, experience with containerization technologies (Docker, Kubernetes), and cloud platforms like AWS, Azure, or GCP for efficient model deployment - Deploying large language models on Azure and AWS clouds, familiarity with services like EC2, S3, SageMaker, Google Cloud ML Engine, and distributed training infrastructure - Working with LLM-Specific Technologies including vector databases, prompt engineering, template management, few-shot and chain-of-thought (CoT) prompting techniques, and fine-tuning/model customization techniques - Knowledge of techniques such as Relevance Engineering and Knowlege Graphs Qualifications Required: - Proficiency in Python programming for ML model development - Experience with open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow - Knowledge of containerization technologies (Docker, Kubernetes) and cloud platforms like AWS, Azure, or GCP - Familiarity with deploying large language models on cloud platforms and distributed training infrastructure Please note that the job description does not include any additional details about the company. Role Overview: You will lead the collaboration with ML Engineers and DevOps Engineers to formulate AI designs that can be built, tested, and deployed through the Route to Live and into Production using continuous integration / deployment. Key Responsibilities: - Model fine-tuning using open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow to improve model performance - Large Language Model Operations (LLMOps) including deploying and managing LLMs on cloud platforms, training, and refining LLMs for specific tasks, scaling LLMs up and down, blue/green deployments, and roll back bad releases - Curating and preparing training data, monitoring and maintaining data quality, transforming, aggregating, and de-duplicating data, making data visible and shareable across data teams, building vector databases, and monitoring LLM performance - Continuous integration and delivery (CI/CD) to automate the model development process, manage infrastructure for distributed model training, and deploy ML models using containerization (Docker) - Utilizing programming languages and frameworks such as Python, LLM pipelines, LangChain, LlamaIndex, experience with containerization technologies (Docker, Kubernetes), and cloud platforms like AWS, Azure, or GCP for efficient model deployment - Deploying large language models on Azure and AWS clouds, familiarity with services like EC2, S3, SageMaker, Google Cloud ML Engine, and distributed training infrastructure - Working with LLM-Specific Technologies including vector databases, prompt engineering, template management, few-shot and chain-of-thought (CoT) prompting techniques, and fine-tuning/model customization techniques - Knowledge of techniques such as Relevance Engineering and Knowlege Graphs Qualifications Required: - Proficiency in Python programming for ML model development - Experience with open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow - Knowledge of containerization technologies (Docker, Kubernetes) and cloud platforms like AWS, Azure, or GCP - Familiarity with deploying large language models on cloud platforms and distributed training infrastructure Please note that the job description does not include any additional details about the company.