Overview

Experience - 8-10 yr

Need relevant resource

Time zone - IST

Key Responsibilities

• LLMs for specific tasks using techniques like parameter-efficient fine-tuning (PEFT) (e.g., LoRA, QLoRA).
• Implementing Retrieval-Augmented Generation pipelines to enhance the knowledge and accuracy of LLMs.
• Utilizing vector databases for efficient storage and retrieval of embeddings generated by LLMs.
• Drafting effective prompts to elicit desired responses from LLMs.
• Connecting LLMs and generative models with other systems and APIs to create comprehensive solutions.
• Communicate findings: Collaborate extensively with data scientists and business during model development and deployment.
• Maintain updated documentation with details of all aspects of model development lifecycle.
• Responsible AI: Build AI systems which are trustworthy and beneficial considering ethical principles such as fairness, transparency, accountability, privacy, and reliability.
• Implement quantifiable metrics detecting bias, explainability, and adherence to regulatory compliance.
• AI Model Deployment and Lifecycle Management: Orchestrate robust and error-free deployment of AI models into production environments, making them accessible to applications and users.
• Ensure that models are deployed securely in compliance with relevant regulations.
• Automation and Pipeline Management: Create and manage automated pipelines for AI workflows including training, testing, and deployment.
• Accelerate the AI model lifecycle ensuring continuous availability of updated and optimized model algorithms, reducing manual errors.
• Implement CI/CD pipelines to automate the testing and deployment of new model versions, enabling updates reducing manual intervention.
• Monitoring and Maintenance: Set up monitoring systems to track key metrics such as prediction accuracy, response times, resource utilization, and error rates of deployed models.
• Identify and troubleshoot issues, ensuring the models continue to perform as expected.
• Infrastructure Management: Manage the infrastructure required for training, testing, and running AI models in production, including provisioning hardware and software resources, leveraging cloud platforms and containerization technologies like Docker and Kubernetes.
• Data and Model Versioning and Rollback: Implement version control for data and models, allowing for tracking changes, testing older versions, and ensuring reproducibility.
• Establish data governance practices and experiment tracking for auditing and compliance purposes.
• Collaboration and Communication: Collaborate extensively with data scientists, software engineers, and DevOps teams to ensure smooth integration of AI models.
• Maintain updated documentation with details of all aspects of model deployment and lifecycle.