Lead Data & AI Platform Engineer

ALO•San Ramon, CA

About The Position

We are looking for a Lead Data & AI Platform Engineer to architect, build, and evolve the foundational platforms that power data science, advanced analytics, and AI across the company. This role is for a deeply experienced builder who has designed production-grade AI systems end to end—data, models, orchestration, evaluation, deployment, and governance—and understands how to make them reusable, reliable, and scalable. You will define the technical backbone for how teams experiment, deploy, and operate AI: from LLM-powered applications and agentic systems to classical ML, analytics, and decision intelligence. This is a hands-on, high-leverage role with broad architectural influence.

Requirements

Platforms & Infrastructure: Cloud-native architectures (AWS, GCP, Azure)
Kubernetes, Docker, CI/CD, infrastructure-as-code
Vector databases, feature stores, workflow orchestration
Data & MLOps: Data pipelines, model lifecycle management, monitoring, experimentation
Offline and online evaluation, A/B testing, human-in-the-loop systems
AI / ML / GenAI: LLMs, RAG systems, agentic frameworks, prompt and model evaluation
Build ecosystem for fine-tuning, hybrid ML + LLM architectures, NLP, multimodal systems
10+ years of experience across data engineering, ML/AI engineering, or platform engineering.
Proven ownership of large-scale, production AI platforms used by multiple teams or products.
Strong foundation in software engineering (Python-centric, APIs, microservices, distributed systems).
Experience supporting consumer-scale or enterprise-scale AI products.
Prior work on AI Centers of Excellence or shared AI platforms.
Background in retail, digital products, supply chain, or customer-facing AI systems.

Responsibilities

Architect and own the Data & AI platform—covering ingestion, feature pipelines, model development, LLM orchestration, evaluation, deployment, and monitoring.
Design scalable GenAI and agentic architectures, including RAG pipelines, tool-using agents, multimodal systems, and hybrid ML + LLM solutions.
Establish platform standards for experimentation, model evaluation, versioning, CI/CD, and production readiness across teams.
Enable fast, safe innovation by creating reusable components, SDKs, templates, and internal tooling for data scientists, ML engineers, and product teams.
Drive build vs. buy decisions for AI platforms, cloud services, vector databases, and orchestration frameworks with a strong cost, scalability, and flexibility lens.
Partner closely with Data Science, Engineering, Product, and Analytics leaders to translate business needs into durable platform capabilities.
Embed responsible AI practices into platform design—privacy, security, auditability, and human-in-the-loop evaluation.
Act as a technical north star—reviewing designs, unblocking complex problems, and mentoring senior engineers across the org.
A Lead engineer who thinks in systems, not just solutions.
Equally comfortable designing architecture, writing production code, and reviewing others’ designs.
Deeply pragmatic—you care about elegance, but you care more about reliability, adoption, and impact.
Someone who has taken AI systems from research or prototype to real users at scale and learned the hard lessons.