About the job
The AI Observability team makes AI, ML, and Agentic systems transparent, reliable, and production-ready at scale. We build end-to-end observability for ML and GenAI workloads, capturing model inputs, features, predictions, outcomes, and behavior across online and batch systems. Our platform enables teams to monitor model performance, data quality, drift, latency, and failures, turning the ML system from a black box into an explainable, debuggable system. We provide developer-friendly libraries, dashboards, and alerts so teams can debug issues, respond to incidents, and ship AI-powered products with confidence.
Responsibilities
Partner with ML researchers, engineers, and platform teams to embed “observability-by-default” into new AI services, ensuring telemetry, monitoring, and evaluation are built into systems from day one.
Lead the end-to-end observability strategy for AI workloads, including LLMs, generative AI systems, and classical ML models; driving build vs. buy decisions, and scaling solutions across model training, online inference, and agent orchestration
Drive the evolution of LLM evaluation frameworks, covering prompt instrumentation, response quality measurement, grounding correctness, hallucination rates, and human/LLM‑as‑a‑judge scoring.
Define and execute a platform roadmap focused on incremental delivery, with clear success metrics, migration goals, and strong adoption across teams.
Communicate progress to stakeholders, customers, and senior leadership.
Hire, grow, and mentor a high-performing engineering team while fostering an inclusive and collaborative culture.
Qualifications
Minimum
10+ years of software engineering experience and 3+ years of management experience.
Experience leading teams responsible for building high-traffic distributed systems and ML infrastructure
Deep familiarity with AI and ML operations, including model evaluation, drift detection, and continuous monitoring at scale.
Experience with AI observability and monitoring tools (e.g., Arize AI, Fiddler AI, Weights & Biases, Vertex AI Model Monitoring, SageMaker Model Monitor)
Exposure to LLM or generative AI systems, including prompt/result logging, evaluation metrics, LLM-as-a-judge frameworks, and human-in-the-loop review
Preferred
Strong technical acumen and can act as a credible technical advisor to the team, set and enforce a high-quality bar for code and system design, and be a mentor for the team.
Strong communication and collaboration skills, and the ability to build strong relationships with internal customers and external partners.
A demonstrated ability to develop, drive, and execute a technical vision and roadmap.
Experience managing a hybrid team with partners and team members distributed across (US) geographies & time zones.