Principal Engineer, API & MCP Platform

Why this role matters:

This role will be instrumental to Babel Street’s AI-native technology strategy. The API and MCP platform will serve as the interface layer between data, AI, and product capabilities, enabling agents, developers, and systems to interact seamlessly. This is a high-impact, senior technical leadership role with broad influence across the organization and the opportunity to shape the future of Babel Street’s platform.

What you will do:

• Partner closely with Architecture to define and lead Babel Street’s API and MCP platform strategy.
• Design and deliver semantic, agent-ready APIs.
• Establish MCP standards and implementation patterns.
• Define context management patterns for agent workflows.
• Partner with AI and Data teams to enable agent-native architectures.
• Establish API governance and platform standards.
• Build foundational frameworks and reusable components.
• Provide hands-on technical leadership and mentorship.
• Partner with Google and other vendors on platform capabilities.
• Drive adoption of API and MCP standards across engineering teams.

What you will bring:

Required Qualifications

• 10+ years of software engineering experience.
• Experience operating at Principal Engineer or Staff+ level.
• Strong hands-on experience building distributed systems.
• Experience designing and building API-first platforms.
• Experience with cloud-native architectures (GCP preferred).
• Experience building developer platforms or service-oriented architectures.
• Strong system design and architectural thinking skills.
• Proven ability to lead cross-team technical initiatives.

Preferred Qualifications

• Hands-on work with Google Cloud Platform (GCP).
• Familiarity with Vertex AI Agent Engine.
• Background in ADK and reasoning frameworks such as LangChain.
• Understanding of the Model Context Protocol (MCP).
• Exposure to data warehouse technologies like BigQuery and Snowflake.
• Knowledge of Apigee or similar API gateway solutions.
• Proven ability to design semantic APIs for AI or agent-driven use cases.
• Experience managing context or building memory systems.
• Development of agent-based architectures or orchestration frameworks.
• Exposure to Anti-Gravity.

What success looks like:

30 Days

• Assess current API and platform architecture.
• Define API and MCP platform strategy.
• Identify early opportunities for standardization.

60 Days

• Deliver initial platform frameworks and standards.
• Begin rollout of semantic API patterns.
• Establish MCP integration approach.

90 Days

• Agent-ready APIs in production.
• MCP-enabled services deployed.
• Clear platform standards adopted across teams.
• Measurable improvement in API consistency and developer velocity.