Principal AI Engineer

Vertex, Inc.; Pennsylvania; Hybrid; USD 159,600 - 207,500; Full time

Job summary

Vertex, Inc. seeks a Principal Engineer to design the central AI system, connecting LLMs to tools, data, and sub-agents. You will shape retrieval, chunking, and how tools are surfaced to models, including MCP servers, with a hands-on approach.

You will mentor engineers, define multi-agent patterns, and balance cost, latency, and safety while collaborating with product teams to onboard capabilities as tools and agents.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related discipline; 12+ years of software/AI engineering experience.
• Hands-on experience building LLM orchestration, agents, and retrieval systems.
• Ability to set strategy and standards while remaining hands-on in code.

Responsibilities

• Design orchestration and abstraction layers connecting LLMs to tools, data, and sub-agents.
• Build and operate MCP servers; define tool exposure and versioning standards.
• Define tool-surface strategy and multi-agent patterns; determine when to use sub-agents vs direct exposure.
• Design retrieval systems (RAG): chunking, embeddings, vector stores, and re-ranking.
• Establish observability and tracing across multi-step agent/tool calls; manage safety and access control.
• Partner with product teams to onboard capabilities as tools and agents; mentor engineers.

Skills

LLM orchestration

Agentic patterns

Observability

Stakeholder collaboration

Cost/latency optimization

Context-window management

Retrieval/RAG

System design

Education

Bachelor's degree in Computer Science or related field

Advanced degree preferred

Tools

LangGraph

LlamaIndex

Semantic Kernel

MCP servers

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 - $207,500.00. 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, a role‑specific sales commission/bonus, and/or equity grants.

Equal Opportunity Employer

Vertex is an affirmative action and equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact 610‑640‑4200 or email [e-mail].