Principal ML Platform Engineer/ Architect

About the Role

Own the vision, architecture, and delivery of our clients enterprise AI platform. You’ll lead a team that powers both agentic workflows and data science at scale — from GPU infrastructure to LLM agents in production. You’re the bridge between boardroom strategy and engineering reality.

Responsibilities

• Lead a team building and operating enterprise AI platforms for agentic workflows and data science at scale.
• Define platform vision and a 3-year AI platform strategy and secure budget from executives (CTO, CDAO, CISO, BU GMs).
• Architect end-to-end ML systems spanning training, inference, RAG, agents, data infrastructure, and multi-tenant secure environments.
• Drive adoption: take ambiguous business problems to platform capabilities and cross-BU or customer adoption.
• Hire, grow, and retain senior ICs and managers; set a high technical bar and cultural bar.

Qualifications

• 3+ years leading teams that ship production AI/ML platforms.
• 5+ years hands-on experience architecting large-scale AI systems: training, inference, RAG, agents, and data infrastructure.
• Full-stack AI expertise across data, model, inference, and platform layers.
• Data & Context: Lakehouse, streaming, feature stores, vector DBs, knowledge graphs, real-time CDC.
• Model layer: fine-tuning LLMs/SLMs with LoRA/QLoRA, RLHF/DPO, distillation, and evaluation harnesses.
• Inference: high-throughput, low-latency serving (vLLM, TGI, Triton), GPU optimization & cost management.
• Agents: multi-agent orchestration, tool use, memory, planning, human-in-the-loop, observability.
• Platform: MLOps/LLMOps, CI/CD for models & prompts, model registry, lineage, security, and guardrails.
• Cloud & infra: deep experience on AWS, GCP, or Azure and Kubernetes; built secure, multi-tenant AI environments for regulated data; on-prem/hybrid GPU clusters a plus.
• Track record taking ambiguous problems → platform capabilities → adoption across multiple BUs or customers.
• Executive influence and partnership with C-suite stakeholders.

Preferred Qualification

• Regulated industry experience (FINRA, HIPAA, PDPA/GDPR, or public sector compliance) and shipping under security constraints.
• 0→1 and 1→100 experience: launched new AI platform capabilities and scaled them to many internal users or enterprise customers.
• Technical depth in distributed systems, databases, or compilers; ability to read/debug CUDA kernels or Ray actors.
• Business fluency: P&L ownership, build-vs-buy decisions, vendor negotiations, TCO modeling for AI infrastructure.
• Advanced degrees (PhD/MS) or active research/open source contributions; patents/publications a plus.
• Strong communication skills: keynote-level speaking and board-deck authorship.