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About This Role
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 (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*.
Salary Context
This $159K-$207K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vertex, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($183K) sits 16% below the category median. Disclosed range: $159K to $207K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Vertex AI Hiring
Vertex has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $207K - $207K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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