Senior Principal AI Engineer

About this role

Job Description:

• 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”…