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 (training, inference, environments, cost/perf), Model & Agent Lifecycle & Governance (CI/CD, registries, lineage, access control), Agent Observability, Evaluation & Reliability (quality, drift, experimentation)
Make agent evaluation and experimentation default platform capabilities, ensuring every production agent and model ships with: Offline evaluation (golden scenarios, regression suites), Pre-deployment quality gates in CI/CD, Controlled experimentation (A/B tests, canaries, shadow traffic), 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
Partner cross-functionally with Applied AI, Data Science, Product, Security, Legal, and Responsible AI to translate business and regulatory requirements into enforceable engineering systems
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
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
Experience building evaluation systems for models and agents, including offline tests, regression suites, online monitoring, and LLM-as-a-Judge–style approaches
Strong background in AI agents and LLM systems, including tool use, multi-step workflows, RAG, prompt and policy management, and common agent failure modes
Experience with data and ML platforms (e.g., Snowflake-centric workflows, feature stores, training pipelines)

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

machine learningartificial intelligenceKubernetesCI/CDdistributed systemsobservabilitygenerative AIML algorithmsHugging FaceTensorFlow

leadershipcross-functional collaborationoperational excellenceorganizational skillscommunication

master's degreePh.D. degree