• Take ownership of the ML API that serves NBA recommendations, partnering with the data engineer who's been building it, and harden it for low-latency production traffic. • Ship your first agent tool contract end-to-end: schema design, handler implementation, structured-error contract, unit tests, deployed via HAL's runtime. • Set up the eval foundation for our agents: golden transcripts, rubric-based judges, regression suites that run on every prompt or model change. • Build a working relationship with HAL and become the data team's go-to on agent infrastructure decisions. • Be the primary owner (with data engineer support) of the ML API and the agent tool layer that wraps NBA and our ML models. • Have shipped at least one production-grade agent (customer-facing or partner-facing) with prompt versioning, evals, observability, and multi-tenant gating in place. • Define the data team's playbook for shipping a new ML model as an LLM-callable tool, end-to-end. • Mentor the data engineers on ML/AI patterns so they can confidently support and extend the systems you own. • Operate as the technical lead within the data team for NBA production AI at Clutch — the person other teams come to when they want to understand how NBA ships ML and agents responsibly. • Have measurably improved agent cost and latency (target: 30%+ reduction on P95 latency or per-conversation cost on at least one agent). • Be shaping the data team's roadmap for the next generation of ML and AI products, in partnership with the PM and data scientist. • Help us decide what to hire next as the team scales.

• Lead the operational architecture, deployment strategy, and reliability engineering for integrating AI into high-stakes Healthcare Information Systems (HIS) • Define the enterprise operational standards, govern the release processes, and build the resilient infrastructure required to maintain models in mission-critical clinical environments • Architect and govern the comprehensive release process, defining enterprise checklists, automated approval gates, release notes, and deployment readiness standards • Establish the deployment execution standards for promoting AI across all environments and ensure customer deployments adhere to strict internal production discipline • Architect and oversee the enterprise model registry, ensuring seamless integration with CI/CD pipelines and full version control traceability • Define and enforce monitoring standards, establishing critical SLAs/SLOs, service health metrics, and comprehensive dashboards across the AI ecosystem • Architect automated checks for input/output data quality and model drift, ensuring proactive detection of system degradation • Establish and lead the production incident process, including rigorous triage workflows, severity escalation paths, postmortems, rollback mechanisms, and recovery infrastructure • Partner with Platform teams to provide essential ATO (Authority to Operate) and compliance support, ensuring complete deployment traceability and strict operational controls • Oversee comprehensive operational reporting, providing leadership with status updates across production systems, pre-prod testing, customer rollouts, and incident metrics • Foster a culture of production discipline, guiding junior engineers in maintaining operational runbooks and reliable deployment pipelines

• Signal detection and anomaly detection • Statistical and ML-based detectors that identify meaningful patterns in portfolio signals — velocity changes, capacity saturation, dependency risks — from CDC event streams and external tool integrations. Not simple threshold alerts; intelligent pattern recognition that knows the difference between noise and a real problem. • Insight synthesis engine • An LLM-powered correlation engine that takes raw signals and produces actionable insights with root causes, confidence scores, and evidence chains. Not just “something is wrong” — but “why it’s wrong, what it means, and what you should do about it.” • Planning Rules compiler • A translation layer between natural language planning rules (written by portfolio managers) and the structured parameters that drive our Monte Carlo scheduling engine. The LLM interprets intent; the deterministic engine computes schedules. You’ll design how these two layers communicate reliably. • Evaluation and testing frameworks • The pipelines that ensure AI outputs are reliable, consistent, and improving over time. Regression suites for prompt changes, A/B testing infrastructure for model updates, confidence calibration — because vibes-based testing doesn’t scale at enterprise scale. • MCP tool definitions • LLM-ready tool specs for domain capabilities (Item Store queries, capacity lookups, scenario simulations) that Tempo AI can discover and invoke at runtime within a hub-and-spoke MCP architecture already in production.

• Own the technical vision and roadmap for ML systems powering forecasting, supply positioning, and fleet optimization. • Lead end-to-end execution of complex, cross-functional ML initiatives. • Act as the primary technical decision-maker for the team. • Mentor and develop engineers, providing technical guidance. • Partner with product and operations leadership to shape strategy. • Establish best practices for ML development, deployment, monitoring, and iteration at scale. • Identify and drive high-leverage opportunities. • Serve as a hands-on technical leader, contributing to critical parts of the codebase.