Senior Lead Data & AI Engineer

Key Responsibilities:

• Architect and build reusable, metadata-driven data and AI engineering frameworks that standardize ingestion, transformation, feature engineering, and AI workflow deployment. Leverage Databricks, lakehouse architecture, declarative pipelines, and cloud-native services to enable scalable, governed, and reusable data products across the organization.

• Architect and deploy Delta Live Tables and Lakeflow jobs on Databricks to automate data processing, AI pipelines, and agent data refresh cycles.

• Leverage Databricks Workflows and Job Orchestration to schedule and monitor AI agent deployments across multiple business workflows.

• Integrate Lakeflow for real-time data stream processing, ensuring AI agents are updated and responsive to live data.

• Ensure seamless orchestration between AI models and data pipelines, using event-driven architectures for real-time inference and deployment.

• Implement and orchestrate AI agents using frameworks such as Agentic systems, AgentOps tooling, and solutions like Agents on Databricks (Agent-bricks).

• Hands-on experience deploying AI agents using RAG, Graph RAG, MCP-enabled integrations, and agent orchestration frameworks such as AgentOps, AgentBricks, LangGraph, or cloud-native orchestration services.

• Manage AI agent lifecycles, monitoring, and scaling using tools like SageMaker, Bedrock, or AI orchestration frameworks on AWS.

• Ensure robust data governance, metadata management, and AI observability through Unity Catalog, AWS Glue, or custom metadata layers.

• Design for scalability and modularity, ensuring AI agents are reusable across multiple business processes.

Qualifications:

• 9–12+ years in data engineering, specializing in AI deployment within cloud ecosystems (Databricks, AWS).

• Hands-on experience deploying AI agents using frameworks like RAG, graph RAG, and orchestrating agents (AgentOps, Agent-bricks, etc.).

• Proficient in AWS AI/ML services (SageMaker, Bedrock) and orchestration tools (MWAA, Step Functions).

• Strong knowledge of lakehouse architecture, Unity Catalog, and data modeling best practices.

• Deep experience in data orchestration, monitoring, and scalable AI-driven workflows.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.