We're looking for an AI Architect to define and lead the technical vision for how AI is designed, built, and deployed across the organization. You'll translate business problems into scalable AI solutions — setting architectural standards, selecting the right models and patterns, and guiding engineering teams from prototype to production. This is a senior, cross-cutting role that blends deep technical judgment with the ability to influence strategy and bring stakeholders along.
What You'll Do
Own the end-to-end architecture for AI and machine learning systems, from data ingestion and feature pipelines to model training, serving, and monitoring.
Design solutions across the AI spectrum — classical ML, deep learning, and generative AI (LLMs, RAG, agentic systems) — choosing the right approach for each problem rather than defaulting to the trend.
Set technical standards and reference architectures for model development, evaluation, deployment, and governance, and ensure teams adhere to them.
Evaluate and select models, frameworks, vendors, and platforms (build-vs-buy decisions), balancing capability, cost, latency, and risk.
Define patterns for LLM-based applications: prompt orchestration, retrieval pipelines, vector stores, guardrails, evaluation harnesses, and feedback loops.
Partner with product, engineering, and business stakeholders to shape the AI roadmap and translate ambiguous requirements into concrete technical designs.
Establish responsible-AI practices — model governance, bias and safety evaluation, data privacy, auditability, and compliance with relevant regulations.
Guide MLOps and platform strategy so models can be deployed, monitored, and retrained reliably at scale.
What You'll Bring
Extensive experience designing and delivering production AI/ML systems, with a track record of architecting solutions that scaled.
Deep understanding of the ML lifecycle: data engineering, feature stores, training, evaluation, deployment, and monitoring.
Hands-on familiarity with generative AI — LLMs, embeddings, RAG, fine-tuning, and agent frameworks — and a clear sense of their limits.
Strong grounding in cloud architecture (AWS, GCP, or Azure) and the tradeoffs of distributed, scalable systems.
The ability to communicate complex technical concepts clearly to both engineers and executives, and to drive alignment across teams.
A pragmatic, outcomes-driven mindset — comfortable making decisions under ambiguity and trading off the ideal against the shippable.
Nice to Have
Experience with AI governance frameworks and emerging regulation (EU AI Act, NIST AI RMF, or similar).
Background in high-scale data platforms and real-time inference systems.
Familiarity with GPU infrastructure and workload orchestration for training and serving.