Principal AI Engineer

Vertex Inc.; United States; Hybrid; USD 160,000 - 208,000; Full time

Job summary

Vertex Inc. is seeking a Principal AI Engineer focused on orchestration and retrieval for its central AI system. You will define how LLMs connect to tools, data, and sub-agents, and establish standards for MCP servers, tool exposure, and retrieval strategies.

You will guide architecture, implement scalable tooling, and mentor engineers to raise maturity across teams, balancing performance, safety, and cost.

Qualifications

• Hands-on experience building LLM orchestration and retrieval systems.
• Additive architecture that scales with multiple teams and tools.
• Strong understanding of context-window management, prompt design, and cost/latency optimization.
• Experience with evaluation, observability, and performance metrics for AI systems.

Responsibilities

• Design orchestration/abstraction layers connecting LLMs to tools, data, and sub-agents.
• Build MCP servers and define tool surfaces, APIs, and versioning.
• Define retrieval/ chunking strategies, embeddings, vector stores, and re-ranking.
• Establish when to use specialized sub-agents versus direct tool exposure.
• Mentor engineers and raise orchestration maturity across teams.

Skills

LLM orchestration

Agentic patterns

Retrieval/RAG

Context/window management

Performance optimization

Education

Bachelor’s degree in CS/Engineering

Advanced degree preferred

Tools

MCP servers

Tool/function-calling integrations

Job description

## Principal AI EngineerApplylocations: Remote USAtime type: Full timeposted on: Posted Yesterdayjob requisition id: JR102525**Job Description:****Job Summary**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.**Essential Job Functions and 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**Knowledge, Skills, and Abilities*** 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**Education and Experience*** 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**Disclaimer**The above statements describe the general nature and level of work performed in this role. Other duties may be assigned.**Pay Transparency Statement:**US Base Salary Range: $159,600.00 - $207,500.00Base pay offered to new hires may vary based upon factors including relevant industry and job-related skills and experience, geographic location, and business needs.\* The range displayed does not encompass the full potential of the role, which allows for further growth and career progression.In addition, as a part of our total compensation package, this role may be eligible for the Vertex Bonus Plan (VOB), a role-specific sales commission/bonus, and/or equity grants.Learn more about Life at Vertex and connect with your recruiter for more details regarding Vertex's compensation and benefit programs.*\*In no case will your pay fall below applicable local minimum wage requirements*.