Lead AI Developer and DevOps

Job Description:

Model Development & Deployment

Model fine-tuning: Use open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow to improve model performance Large Language Model Operations (LLMOps)

Model deployment and maintenance: deploying and managing LLMs on cloud platforms

Model training and fine-tuning: training and refining LLMs to improve their performance on specific tasks

work out how to scale LLMs up and down, do blue/green deployments and roll back bad releases

Data Management & Pipeline Operations

Curating and preparing training data, as well as monitoring and maintaining data quality

Data prep and prompt engineering: Iteratively transform, aggregate, and de-duplicate data, and make the data visible and shareable across data teams

Building vector databases to retrieve contextually relevant information

Monitoring & Evaluation

Monitoring and evaluation: tracking LLM performance, identifying errors, and optimizing models

Model monitoring with human feedback: Create model and data monitoring pipelines with alerts both for model drift and for malicious user behavior

Establish monitoring metrics

Infrastructure & DevOps

Continuous integration and delivery (CI/CD), where CI/CD pipelines automate the model development process and streamline testing and deployment

Develop and manage infrastructure for distributed model training (e. g. , SageMaker, Ray, Kubernetes). Deploy ML models using containerization (Docker)

Required Technical Skills

Programming & Frameworks

Use open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow

LLM pipelines, built using tools like LangChain or LlamaIndex

Python programming expertise for ML model development

Experience with containerization technologies (Docker, Kubernetes)

Cloud Platforms & Infrastructure

Familiarity with cloud platforms like AWS, Azure, or GCP, including knowledge of services like EC2, S3, SageMaker, or Google Cloud ML Engine for scalable and efficient model deployment

Deploying large language models on Azure and AWS clouds or services such as Databricks

Experience with distributed training infrastructure

LLM-Specific Technologies

Vector databases for RAG implementations

Prompt engineering and template management

Techniques such as few-shot and chain-of-thought (CoT) prompting enhance the models accuracy and response quality

Fine-tuning and model customization techniques

Knowlege Graphs

Relevance Engineering

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