Scrum Master
2 weeks ago
Key Responsibilities :
Agile Delivery Leadership :
- Facilitate daily stand-ups, sprint planning, retrospectives, and backlog refinement sessions.
- Ensure sprint goals are aligned with overall GenAI roadmap and business objectives.
- Track team velocity, manage impediments, and promote continuous improvement.
GenAI Program Coordination :
- Collaborate with Product Owners to define and prioritize user stories involving LLM integration, AI pipelines, and API-driven AI services.
- Manage dependencies between AI model training teams, data pipeline engineers, and cloud deployment units.
- Support model lifecycle management (from data ingestion to fine-tuning and deployment).
Stakeholder Engagement :
- Communicate sprint progress, risks, and achievements to leadership and non-technical stakeholders.
- Partner with AI Architects, MLOps Engineers, and UX teams for smooth cross-functional execution.
- Translate complex GenAI development updates into business-friendly progress reports.
Process and Governance :
- Implement Agile frameworks (Scrum, Kanban, or SAFe) in AI-centric environments.
- Promote responsible AI governanceprivacy, ethics, and bias mitigation in model use.
- Use Agile tools (e.g., Jira, Azure DevOps) for sprint tracking and metrics visualization.
Required Skills and Qualifications :
Core Scrum Expertise :
- Certified Scrum Master (CSM), PSM I/II, or SAFe Scrum Master certification.
- Proven experience managing 2+ Agile teams delivering Data, AI, or Cloud solutions.
AI/GenAI Understanding :
- Familiarity with Generative AI conceptsLLMs (e.g., GPT, Claude, Gemini), prompt engineering, RAG, vector databases, and AI orchestration tools.
- Exposure to MLOps, AI pipelines, or data engineering workflows.
- Understanding of cloud-based AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI).
Tools and Collaboration :
- Azure DevOps for Agile operations.
- Exposure to tools like LangChain, CrewAI, or OpenAI API integrations preferred.
Soft Skills :
- Strong communication, servant leadership, and conflict-resolution skills.
- Ability to drive clarity and focus in technically complex AI environments.
- Data-driven decision-making mindset with innovation agility.
Nice-to-Have Skills :
- Experience in AI product lifecycle management or AI governance frameworks.
- Background in software engineering, data science, or cloud architecture.
- Exposure to Agile scaling frameworks (SAFe, LeSS, Spotify Model).
- Familiarity with AI evaluation metrics, model drift tracking, and human-in-the-loop processes.
KPIs / Success Metrics :
: - Sprint velocity and delivery predictability.
- Reduced AI model deployment cycle times.
- Improved backlog health and prioritization accuracy.
- Stakeholder satisfaction and cross-team alignment.
- Implementation of responsible AI practices
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