Scrum Master

About the Company :

Everest DX – We are a Digital Platform Services company, headquartered in Stamford. Our Platform / Solution includes Orchestration, Intelligent operations with BOTs’, AI-powered analytics for Enterprise IT. Our vision is to enable Digital Transformation for enterprises to deliver seamless customer experience, business efficiency and actionable insights through an integrated set of futuristic digital technologies.

Digital Transformation Services - Specialized in Design, Build, Develop, Integrate, and Manage cloud solutions and modernize Data centers, build a Cloud-native application and migrate existing applications into secure, multi-cloud environments to support digital transformation. Our Digital

Platform Services enable organizations to reduce IT resource requirements and improve productivity, in addition to lowering costs and speeding digital transformation.

Digital Platform - Cloud Intelligent Management (CiM) - An Autonomous Hybrid Cloud Management Platform that works across multi-cloud environments. helps enterprise Digital Transformation get most out of the cloud strategy while reducing Cost, Risk and Speed.

To know more please visit :

http : / / www.everestdx.com

Job Title : GenAI Scrum Master

Job Location : Hyderabad / Chennai

Job Type : Full Time

Experience : 7+ years (including 2+ years in AI / GenAI or Data projects)

Notice Period - Immediate to 15 days joiners are highly preferred

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 governance—privacy, 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 concepts—LLMs (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.