Director of AI & Data Platform

Responsibilities

• Own the strategy, roadmap, and execution for the AI Platform & Data pillar, including MCP servers, agent runtime and orchestration, AI-native data platforms, analytics products, and governance.
• Lead a multi-team organization spanning orchestration, data engineering, analytics, and governance; recruit, mentor, and develop engineering managers and technical leads.
• Build an AI-native data platform with CDC pipelines, LLM-ready semantic layers, lineage, freshness SLAs, and data-quality frameworks.
• Reinvent analytics through natural-language analytics, autonomous deep dives, anomaly detection, and proactive business-signal surfacing.
• Define architectural patterns for AI-powered internal workflows across Finance, HR, Legal, CX, Supply Chain, and GTM Systems.
• Own the private LLM strategy, including model selection, fine-tuning and post-training, secure deployment, inference infrastructure, evaluation, observability, and lifecycle management.
• Establish workload-routing decisions between private and approved external foundation models based on data classification, security, latency, quality, and cost.
• Govern training and evaluation data pipelines, including curation, anonymization, access controls, versioning, benchmarking, red-team testing, and data-leakage prevention.
• Partner with Security, Engineering, Legal, and Product on private-model use cases.
• Build MCP servers, data connectors, and agent primitives that reduce shadow IT.
• Establish governance for model access, data classification, audit, evaluation, and cost controls.
• Drive AI-assisted SDLC, agentic coding, and internal-tooling velocity.
• Represent the pillar to executive leadership, business-function heads, and external partners.

Requirements

• 10+ years of experience in software, data, or platform engineering.
• 6+ years leading engineering teams and at least 2 years managing managers.
• Track record building and shipping platform products at scale, such as internal developer platforms, AI/ML infrastructure, or modern data platforms.
• Fluency with LLMs, RAG, agent frameworks, MCP, prompt engineering, and evaluation/observability.
• Experience with private LLM deployment and optimization, including open-weight models, supervised fine-tuning, preference optimization, quantization, inference serving, GPU infrastructure, model evaluation, and secure model operations.
• Experience designing hybrid AI architectures that route requests between private and commercial foundation models.
• Strong data engineering and analytics foundation, including lakehouse architectures, CDC, streaming, dbt-style transformation, semantic layers, and analytics-grade data quality.
• Experience building systems that pass security audits.
• Ability to partner with non-technical business functions and translate ambiguous requirements into platform capabilities.
• Excellent written and verbal communication.

Nice to Have

• Cybersecurity, critical-infrastructure, or regulated-industry experience.
• Experience fine-tuning or post-training open-weight models for enterprise, cybersecurity, software engineering, or domain-specific use cases.
• Familiarity with vLLM, SGLang, Hugging Face, NVIDIA NIM, Kubernetes-based GPU serving, or equivalent tools.
• Experience automating revenue operations, finance, HR, or legal workflows through AI.
• Familiarity with SugarCRM, Salesforce, or comparable enterprise-system metadata and integration patterns.
• Experience deploying MCP servers, agent infrastructure, or AI gateways in production.
• Perspective on how AI is changing software delivery for internal engineering teams.