EverestDX - Scrum Master - Agile Methodologies

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