Principal AI Engineer at Vertexinc

Job description

Job Description: 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. Vertex Values: Together We Win We re building a team of people who are passionate about making an impact for our customers and committed to how that impact is achieved. Our values define the behaviors, mindset, and culture that make Vertex a great place to grow and do meaningful work. Play to Win or We Don t Play — If we choose to do something, we re choosing to do it because we plan to win. That mindset raises our bar on product quality, customer outcomes, and how we show up for one another. Work As a Team, Putting the Customer At the Core — Our customers are our true north. Whatever your role, ask: how will this help a customer succeed today? We earn trust through outcomes, not promises. Achieve Excellence With Integrity, Speed, and Agility — The market isn t slowing down. We ll move faster, adapt quickly, and never compromise on doing things the right way — for teammates, customers, and partners. Innovate Boldly With a Growth Mindset — Progress demands smart risk. We ll try new approaches, learn fast, and keep pushing the boundaries — especially where AI can remove friction and unlock value. Communicate with Care, Candor and Transparency — Honest, constructive conversations make us better. Let s speak plainly about what s working and what isn t and help each other improve. Pay Transparency Statement: US Base Salary Range: $159,600.00 - $207,500.00 Base 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 .

Requirements

Must-haves

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

Nice-to-haves

Bachelor’s degree in Computer Science, Engineering, or related discipline; advanced degree preferred
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.

Application prep

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Keywords to mirror

aillm

Platform: workday
Effort: long