Senior Director, AI Engineering (AI)

TL;DR

Sr. Director - AI Engineering: Leading the engineering of AI Foundations team that enables teams to build, deploy, evaluate, experiment on, monitor, and govern AI agents and ML models safely and at enterprise scale with an accent on ML infrastructure, agent lifecycle management, evaluation, observability, and governance. Focus on interoperability across platform components, operationalizing a data mesh–oriented architecture, and ensuring the platform delivers high reliability, strong security, and cost-efficient scalability.

Location: New York, Seattle, Bellevue, San Francisco.

Company

hirify.global’s AI Foundations team is foundational to enabling both traditional machine learning applications and AI agents at enterprise scale.

What you will do

• Lead the engineering of AI Foundations team that enables teams to build, deploy, evaluate, experiment on, monitor, and govern AI agents and ML models safely and at enterprise scale.
• Own three core platform areas: ML Platform & Developer Productivity, Model & Agent Lifecycle & Governance, Agent Observability, Evaluation & Reliability.
• Make agent evaluation and experimentation default platform capabilities, ensuring every production agent and model ships with: Offline evaluation, Pre-deployment quality gates in CI/CD, Controlled experimentation, Continuous post-deployment monitoring.
• Drive end-to-end observability across data pipelines, retrieval, model inference, tool execution, and agent outcomes, with clear SLIs/SLOs for quality, latency, reliability, and cost.
• Standardize ML and agent development workflows, reducing time-to-production and eliminating bespoke infrastructure across teams.
• Build and lead a high-performing organization of engineering managers and senior engineers, setting a strong technical bar and culture of operational excellence.

Requirements

• 15+ years of engineering experience, with 7+ years leading platform or infrastructure teams in ML, data, or AI-heavy environments.
• A master's or Ph.D. degree in computer science, machine learning, artificial intelligence, or equivalent industry experience.
• Proven experience with ML and platform infrastructure, including Kubernetes-based systems, CI/CD, distributed systems, and observability stacks (metrics, logs, tracing).
• Expertise in generative AI, ML algorithms, and frameworks such as Hugging Face, Tensorflow, PyTorch, etc.
• Experience with cloud platforms (e.g., AWS, GCP) and distributed computing frameworks (e.g., Spark, Hadoop).
• Hands-on familiarity with experimentation frameworks, such as A/B testing, canaries, and shadow deployments, and integrating experiments into ML/agent pipelines.