Senior Principal AI Engineer

This position is a senior-level role responsible for leading the design, implementation, and maintenance of scalable, secure, and high-performance software systems across multiple projects. The position emphasizes architectural leadership, technical mentorship, and strategic collaboration to drive innovation, ensure consistency, and align solutions with organizational goals

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.

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.

• 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),…