Senior Principal AI Engineer

• Serve as the most senior individual-contributor engineer and principal technical authority within the Commercial AI Center of Excellence (CAI CoE), owning 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 — able to go as deep as any specialist and to compose the pieces into coherent, production-grade systems. 

• This role requires both traditional AI/ML, GenAI product engineering, and traditional software architecture skillsets. 

• 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 the most complex, ambiguous, cross-team initiatives spanning multiple value streams, and review and influence AI designs across product teams to ensure alignment with enterprise standards. 

• Set long-term technical direction while remaining deeply hands-on with the most critical AI infrastructure and services. 

Key Responsibilities 

• Set the multi-year technical vision, reference architectures, and enterprise standards for AIaaS across PDD, and act as the final technical authority and escalation point for the organization's hardest AI problems. 

• Define and own the enterprise model-training strategy across traditional AI/ML and LLMs, and personally train, fine-tune (e.g., QLoRA, LoRA, PEFT, and full fine-tuning), and evaluate models when the problem demands it. 

• Establish standards for how and where training data from Commercial AI products is sourced, cleaned, versioned, stored, and governed (lineage, licensing/consent, and PII), and architect the large-scale data and feature pipelines behind them. 

• Architect the orchestration and abstraction layers of the central AI system that connect LLMs to tools, data, and sub-agents, and set standards for MCP servers, tool-surface design (optimal number of tools exposed per LLM and APIs per server), and when to use specialized sub-agents versus direct tool exposure. 

• Design retrieval/RAG systems end to end — chunking strategies, embeddings, vector stores, hybrid search, re-ranking, context assembly, and memory. 

• Own the enterprise evaluation, observability, and safety strategy for AI systems, including offline and online evaluation, tracing, red-teaming, guardrails, and responsible-AI and compliance requirements. 

• Drive build-versus-buy, model and vendor selection, and long-term architectural bets, anticipating where the field is heading and preparing the organization to adopt it. 

• Optimize the performance, cost (token and inference economics), scalability, and reliability of AI workloads in partnership with Security, Cloud Platform, and SRE teams. 

• Multiply the organization: mentor and grow Principal and Staff engineers and raise the AI engineering bar. 

Required Qualifications 

• 15+ years in AI/ML software engineering with demonstrated Senior Principal-level (or equivalent) impact delivering production AI at enterprise scale. 

• Full-lifecycle, hands-on mastery across both specialist domains: (a) training and fine-tuning traditional AI/ML models and LLMs — including parameter-efficient methods (QLoRA/LoRA/PEFT), 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 and feature pipelines. 

• Strong command of data management and governance: dataset storage architecture, 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 that many teams depend on. 

• Rigorous, demonstrated 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. 

• Demonstrated ability to set technical strategy and 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 a related discipline; advanced degree preferred. An equivalent combination of education, training, and relevant professional experience is accepted in lieu of a formal degree. 

• Industry experience in tax or other 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, and model evaluation). 

• Contributions to open-source AI tooling, published research, patents, or recognized technical thought leadership. 

• Strong executive communication and technical storytelling skills. 

Disclaimer:

The above statements describe the general nature and level of work performed in this role. Other duties may be assigned.

Other Qualifications:
The Winning Way behaviors that all Vertex employees need in order to meet the expectations of each other, our customers, and our partners.

•Communicate with Clarity - Be clear, concise and actionable. Be relentlessly constructive. Seek and provide meaningful feedback.

•Act with Urgency - Adopt an agile mentality - frequent iterations, improved speed, resilience. 80/20 rule – better is the enemy of done. Don’t spend hours when minutes are enough.

•Work with Purpose - Exhibit a “We Can” mindset. Results outweigh effort. Everyone understands how their role contributes. Set aside personal objectives for team results.

•Drive to Decision - Cut the swirl with defined deadlines and decision points. Be clear on individual accountability and decision authority. Guided by a commitment to and accountability for customer outcomes.

•Own the Outcome - Defined milestones, commitments and intended results. Assess your work in context, if you’re unsure, ask. Demonstrate unwavering support for decisions.

Comments:

The above statements are intended to describe the general nature and level of work being performed by individuals in this position. Other functions may be assigned, and management retains the right to add or change the duties at any time.

US Base Salary Range: $229,800.00 - $298,700.00

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 aboutLife 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.