AI/ML Engineer

Roles and Responsibilities:

• Designs, develops, and deploys AI/ML models including training, evaluation, and performance optimization.
• Builds and implements intelligent AI agents using large language models and tool integrations.
• Utilizes Agent Development Kit (ADK) to develop, test, and deploy scalable AI agent solutions.
• Deploys and manages AI agents using Agent Engine within Google Cloud environments.
• Develops and deploys containerized applications using Google Cloud Run to support AI services.
• Leverages Vertex AI for end-to-end machine learning lifecycle including model training, deployment, and monitoring.
• Prepares and processes data for model training, ensuring high-quality datasets for optimal model performance.
• Selects appropriate algorithms and performs hyperparameter tuning to improve model accuracy and efficiency.
• Monitors model performance and continuously refines models based on feedback and evaluation metrics.
• Integrates AI/ML solutions into enterprise applications and workflows.
• Collaborates with cross-functional teams to design scalable and production-ready AI systems.
• Maintains documentation for models, agents, and deployment processes to ensure reproducibility and governance.
• Ensures adherence to best practices in AI development, security, and compliance.

Skills Required:

• Expertise in Artificial Intelligence and Machine Learning, encompassing model development, training, evaluation, and deployment.
• Build agents: This refers to the creation of intelligent agents, which are software entities designed to perform specific tasks autonomously, often utilizing large language models (LLMs) and tools to interact with their environment.
• ADK (Agent Development Kit): This specifies the use of Google's ADK, a framework and set of tools for developing, testing, and deploying AI agents, especially on Vertex AI.
• Agent Engine: This points to the runtime environment for deploying and managing AI agents, likely within the Vertex AI Agent Engine on Google Cloud.
• cloudRUN: This suggests the use of Google Cloud Run, a serverless platform for deploying containerized applications, potentially used for hosting components of the AI agents or related services.
• VertexAI: This signifies the central platform for all AI/ML activities, including model training, deployment, and the management of AI agents and their components.
  • Model training: This highlights the crucial aspect of developing and refining machine learning models that power the AI agents, involving data preparation, algorithm selection, hyperparameter tuning, and performance optimization.