Location: Chicago, IL (Local)
Experience: 10+
VISA: TN, L2, (No H1B & OPT)
Job Overview
We are seeking an experienced Scrum Master to guide and enable multi-disciplinary AI Pods (comprising Data Scientists, ML Engineers, Prompt Engineers, Data Engineers, and Software Developers). In this role, you will facilitate Agile practices tailored to the non-linear, experimental nature of AI/ML software development. You will manage cross-functional dependencies, help the team navigate research unpredictability, and ensure the seamless delivery of enterprise-grade AI products into production.
Key Responsibilities
· Agile Facilitation: Lead core Scrum ceremonies (Sprint Planning, Daily Stand-ups, Backlog Refinement, Sprint Reviews, and Retrospectives) adapted specifically for AI model development and software integration cycles.
· AI Pod Operations: Help the team balance exploratory research (spikes, model training, prompt iteration) with predictable engineering deliverables (API integrations, UI deployment).
· Dependency & Risk Management: Manage complex cross-pod dependencies across data platform teams, cloud infrastructure, AI governance/ethics boards, and business stakeholders.
· Impediment Removal: Proactively remove blockers related to data pipelines, GPU compute availability, API limits, model validation delays, and cross-team alignment.
· Stakeholder Alignment & Metrics: Translate AI uncertainty (e.g., accuracy tradeoffs, latency constraints) into business terms for product managers and leaders. Track velocity, cycle times, model deployment cadence, and sprint health.
· Continuous Improvement: Foster an experimental mindset, encourage continuous learning, and refine agile practices based on retrospective feedback.
Key Requirements & Qualifications
· 4+ years of Scrum Master experience, with at least 1–2 years embedded within AI, ML, Data Science, or GenAI engineering teams.
· Certified ScrumMaster (CSM), Advanced CSM (A-CSM), PSM II, or SAFe Scrum Master certified.
Solid conceptual understanding of the AI/ML lifecycle (Data Collection Feature Engineering Model Training Fine-Tuning/RAG MLOps Deployment).
· Expert proficiency in JIRA, Confluence, Miro, or Azure DevOps configured for custom AI workflow tracking.
Familiarity with AI guardrails, model validation processes, data privacy compliance, and production testing approaches.
Preferred / Nice-to-Have Skills
· Familiarity with MLOps frameworks, LLM deployment pipelines, or vector databases.
· PMI-ACP, SAFe POPM, or Agile Coaching certifications.
· Background in software engineering, data engineering, or technical project management.
· Experience navigating cloud AI ecosystems (AWS SageMaker, Azure OpenAI, Google Cloud Platform Vertex AI).
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