Principal AI Engineer

Vertex Inc.•Remote - PA, PA

6d•$159,600 - $207,500

About The Position

The Principal Engineer, AI Orchestration & Retrieval defines how the enterprise's central AI system is composed – the orchestration and abstraction layers that connect LLMs to tools, data, and one another, and the retrieval systems that ground them. This role sets the strategy and builds the reality for how we build and expose tools (including MCP servers), how we structure retrieval and chunking, and when to rely on specialized sub-agents versus directly exposing tools to a model.

Requirements

Deep hands-on experience with LLM orchestration frameworks (e.g., LangGraph, LlamaIndex, Semantic Kernel, or equivalents) and agentic patterns
Direct experience building MCP servers and tool/function-calling integrations
Evidence-based opinions on the optimal number of tools to expose to an LLM and the optimal number of APIs per MCP server, and on overall tool-surface design
A clear, defensible point of view on specialized sub-agents versus direct tool exposure, and the tradeoffs of each
Deep experience with retrieval/RAG: chunking strategies, embeddings, vector databases, hybrid search, and re-ranking
Experience designing abstraction layers and platform APIs that many teams build on top of
Strong understanding of context-window management, prompt/context assembly, and cost/latency optimization
Experience with evaluation and observability for agentic and retrieval systems
Ability to set strategy and standards while remaining hands-on in code
Strong stakeholder collaboration and problem-solving skills
Bachelor’s degree in Computer Science, Engineering, or related discipline; advanced degree preferred
12 or more years of experience in software/AI engineering, with hands-on experience building LLM orchestration, agents, and retrieval systems

Responsibilities

Design the orchestration and abstraction layers of the central AI system that connect LLMs to tools, data, and sub-agents
Design, build, and operate MCP (Model Context Protocol) servers and set standards for how tools are defined, exposed, and versioned
Define tool-surface strategy: the optimal number of tools exposed to an LLM, the optimal number of APIs per MCP server, and how to keep tool surfaces coherent and discoverable
Establish when to use specialized sub-agents versus directly exposing tools to a model, and design the corresponding multi-agent patterns
Design retrieval (RAG) systems: chunking strategies, embedding models, vector stores, hybrid/keyword search, re-ranking, and context assembly
Define abstraction layers that decouple product teams from the underlying models, tools, and providers
Build routing, context-window management, and memory strategies for agentic workflows
Define evaluation for orchestration and retrieval quality (retrieval precision/recall, tool-selection accuracy, task success, latency, and cost)
Establish observability and tracing across multi-step agent and tool calls
Address safety, guardrails, authentication, and access control across tools and agents
Partner with product teams to onboard their capabilities as tools and agents into the central AI system
Mentor engineers and raise orchestration and retrieval maturity across teams