EverestDX - Scrum Master - Agile Methodologies

2 weeks ago


Hyderabad, India EverestDX Full time

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 (ref:hirist.tech)


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