Note: The job is a remote job and is open to candidates in USA. MethodHub is seeking a highly skilled Artificial Intelligence Engineer. The role involves designing and deploying sophisticated AI agents to automate complex business workflows and ensure operational efficiency.
Responsibilities
- Develop intelligent agents using Vertex AI Agent Builder to automate complex business workflows
- Leverage the (Agent Developer Kit) ADK to build and manage multi-agent systems that collaborate to solve end-to-end business challenges
- Implement tools like MCP (Model Context Protocol) Toolbox to securely connect agents to enterprise databases like BigQuery and Spanner
- Utilize Vertex AI for model training, tuning, and deployment, ensuring seamless integration with BigQuery for feature engineering
- Build and optimize streaming data pipelines (e.g., via Dataflow) to execute real-time inference using Run Inference API or Vertex AI endpoints
- Ground AI models in live business context using vector engines within BigQuery or AlloyDB to eliminate "AI amnesia"
- Show up promptly for all internal and client-facing meetings
- Provide regular, structured status updates to team members and stakeholders regarding project milestones and technical blockers
- Demonstrate the ability to ask for help when facing technical hurdles and contribute to a collaborative troubleshooting environment
- Navigate corporate environments to translate high-level business goals into robust technical architectures
Skills
- Proven experience with Model Garden, Vertex AI Pipelines, and model evaluation
- Advanced knowledge of SQL for BigQuery, Python for ML engineering, and data preprocessing techniques (scaling, encoding, imputation)
- Hands-on experience with Google Cloud Storage and Vertex AI endpoints
- Familiarity with stateful real-time processing and the latest innovations in agentic architectures
- Develop intelligent agents using Vertex AI Agent Builder to automate complex business workflows
- Leverage the (Agent Developer Kit) ADK to build and manage multi-agent systems that collaborate to solve end-to-end business challenges
- Implement tools like MCP (Model Context Protocol) Toolbox to securely connect agents to enterprise databases like BigQuery and Spanner
- Utilize Vertex AI for model training, tuning, and deployment, ensuring seamless integration with BigQuery for feature engineering
- Build and optimize streaming data pipelines (e.g., via Dataflow) to execute real-time inference using Run Inference API or Vertex AI endpoints
- Ground AI models in live business context using vector engines within BigQuery or AlloyDB to eliminate 'AI amnesia'
- Active Participation: Show up promptly for all internal and client-facing meetings
- Transparent Communication: Provide regular, structured status updates to team members and stakeholders regarding project milestones and technical blockers
- Proactive Collaboration: Demonstrate the ability to ask for help when facing technical hurdles and contribute to a collaborative troubleshooting environment
- Consultative Approach: Navigate corporate environments to translate high-level business goals into robust technical architectures
- Background in financial services or retail to better understand industry-specific data logic (e.g., credit risk, royalty forecasting, or search relevance)
- Knowledge of privacy and compliance standards for handling PII through masking and redaction
Company Overview
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