Senior Principal AI Engineer

Vertex, Inc.; Pennsylvania; Hybrid; USD 229,800 - 298,700; Full time

Job summary

Vertex, Inc. is seeking a highly senior AI engineer to serve as the principal technical authority for the Commercial AI Center of Excellence.

You will own the AIaaS enablement strategy, spanning data, model training, retrieval, and production operations, while setting enterprise standards for orchestration and governance. You will lead cross-team initiatives, guide tool and MCP design, and influence architectural decisions across value streams, ensuring scalable, responsible AI across production

Qualifications

• 15+ years in AI/ML software engineering with enterprise-scale impact.
• Deep expertise across traditional AI/ML and LLM orchestration in production environments.
• Strong data governance, lineage, licensing, and PII handling expertise.
• Experience designing developer platforms, APIs, and multi-agent orchestrations.

Responsibilities

• Set multi-year AIaaS vision and enterprise standards.
• Define enterprise model-training strategy and personally train/fine-tune models when needed.
• Establish data sourcing, governance, and large-scale pipelines.
• Architect LLM-to-tools/data/sub-agents orchestration and MCP server patterns.
• Design end-to-end retrieval/RAG systems and evaluation/observability strategy.
• Drive build-vs-buy decisions and long-term architectural bets within regulated SaaS.

Skills

Distributed systems

Cloud-native architecture

LLM orchestration

Retrieval/RAG

Model training & fine-tuning

Responsible AI & guardrails

Cost/perf optimization

Mentoring engineers

Regulated SaaS understanding

Education

Bachelor's degree in CS/Engineering

Advanced degree preferred

Tools

Azure Cloud

Vector databases

MCP servers

Job description

Overview

Serve as the most senior individual-contributor engineer and principal technical authority within the Commercial AI Center of Excellence (CAI CoE). Own the technical vision for AI-as-a-Service (AIaaS) enablement at enterprise scale. Operate as a full-spectrum AI engineer fluent across the entire lifecycle — data, model training and fine-tuning, retrieval, orchestration, evaluation, and production operations — capable of deep work across traditional AI/ML, GenAI product engineering, and software architecture. Establish reference architectures, paved-road patterns, and enterprise technical standards for agentic orchestration, tool and MCP design, retrieval, model training, and responsible AI across production systems. Lead complex, cross-team initiatives spanning multiple value streams and influence AI designs to align with enterprise standards. Set long-term technical direction while remaining hands-on with critical AI infrastructure and services.

Responsibilities

• Set the multi-year technical vision, reference architectures, and enterprise standards for AIaaS across the organization; act as the final technical authority and escalation point for the hardest AI problems.
• Define and own the enterprise model-training strategy across traditional AI/ML and LLMs; personally train, fine-tune (e.g., QLoRA, LoRA, PEFT, and full fine-tuning), and evaluate models when needed.
• Establish standards for data sourcing, cleaning, versioning, storage, and governance (lineage, licensing/consent, and PII); architect large-scale data and feature pipelines.
• Architect the orchestration and abstraction layers that connect LLMs to tools, data, and sub-agents; set standards for MCP servers and tool-surface design and determine when to use specialized sub-agents versus direct tool exposure.
• Design end-to-end retrieval/RAG systems (chunking strategies, embeddings, vector stores, hybrid search, re-ranking, context assembly, memory).
• Own enterprise evaluation, observability, and safety strategy for AI systems, including offline/online evaluation, tracing, red-teaming, guardrails, and responsible-AI and compliance requirements.
• Drive build-versus-buy decisions, model and vendor selection, and long-term architectural bets to position the organization for future AI advances.
• Optimize performance, cost (token and inference economics), scalability, and reliability of AI workloads in partnership with Security, Cloud Platform, and SRE teams.
• Mentor and grow Principal and Staff engineers and raise the AI engineering bar across the organization.

Required Qualifications

• 15+ years in AI/ML software engineering with demonstrated Senior Principal-level impact delivering production AI at enterprise scale.
• Full lifecycle mastery across both specialist domains: (a) training and fine-tuning traditional AI/ML models and LLMs (including parameter-efficient methods, quantization, distributed training, and rigorous evaluation); and (b) LLM orchestration, agentic systems, tool/MCP design, and retrieval/RAG in production.
• Deep expertise in distributed systems, cloud-native architecture, and large-scale data/feature pipelines.
• Strong command of data management and governance: dataset storage, versioning, lineage, quality, PII handling, and licensing/consent for training data.
• Proven ability to design developer platforms, APIs, reusable SDKs, MCP servers, and multi-agent orchestrations used by many teams.
• Rigorous approach to AI evaluation, observability and tracing, and responsible-AI guardrails.
• Expertise with cloud platforms (Azure strongly preferred) and a track record of optimizing AI workload cost, performance, scalability, and reliability.
• Ability to influence decisions across many teams without direct authority and to mentor Principal- and Staff-level engineers.
• Experience operating in regulated SaaS environments and meeting security and compliance requirements.

Preferred Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related discipline; advanced degree preferred. An equivalent combination of education, training, and experience accepted.
• Industry experience in regulated domains (insurance, fintech, or healthcare).
• Experience with vector databases and retrieval optimization at scale.
• FinOps for GenAI: experience modeling and optimizing LLM token and inference costs.
• Data science or classical AI background beyond prompt engineering (statistics, feature engineering, model evaluation).
• Contributions to open-source AI tooling, research, patents, or recognized technical thought leadership.
• Strong executive communication and technical storytelling skills.

Compensation and Benefits

US Base Salary Range: $229,800.00 - $298,700.00. Base pay offered to new hires may vary based on relevant industry, job-related skills and experience, geographic location, and business needs. The range does not encompass the full potential of the role, which allows for further growth and career progression. The role may be eligible for the Vertex Bonus Plan (VOB), a role-specific sales commission/bonus, and/or equity grants. In addition, Vertex offers benefits and a total compensation program. Eligibility and details vary by location.

Pay considerations include applicable local minimum wage requirements. Learn more about Life at Vertex and connect with your recruiter for details regarding Vertex compensation and benefits programs.

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. For accessibility assistance or accommodation requests, contact [e-mail] or 610-640-4200.

Disclaimer: The above statements describe the general nature and level of work performed in this role. Other duties may be assigned; management reserves the right to add or change duties at any time.