- Process Hotspot Identification: Identify high-value business processes where Agentic AI can eliminate friction and unlock measurable value, using process analysis, business process mapping, process mining, and value-led prioritisation frameworks across target industries.

- Agentic AI Design &
- Architecture:

Design agentic workflows, multi-agent orchestration, and AI agent architecture for production deployment. Demonstrate deep expertise in LLM architecture, prompt engineering, GenAI platforms, and framework selection (LangChain, AutoGen, CrewAI, or equivalent).

- Enterprise AI &

- GenAI Strategy:

Design & guide development of enterprise-wide AI &
- GenAI strategy for clients. Leverage strong expertise and certification in cloud AI platforms Google Vertex AI / Gemini, Azure AI / Copilot, Claude, Amazon Bedrock, IBM Watson — as well as open-source models.

- Scaled AI Deployment &

- MLOps:

Guide implementation from advisory and prototype through to production-grade, scaled AI deployment, covering AI productionisation, MLOps, AI governance, model monitoring, change management, and value realisation frameworks.

- Responsible AI &

- Governance:

Have a deep understanding of Responsible AI &
- GenAI frameworks, including bias detection, explainability, fairness, and regulatory compliance, to engage clients in meaningful discussions and steer towards the right recommendations.

- Business Case &

- Value Realization:

Define an AI use case driven value realization framework and build quantitative and qualitative business cases relevant to the client's industry. Benchmark against global research and leading industry peers.

- Proof of Concepts &

- Prototyping:





Help design and lead POCs and high-level solutions using AI or GenAI analytics to derive insights from data. Collaborate with business experts, platform engineers, and technology teams for prototyping and client implementations.

- Discovery Workshops &

- Stakeholder Engagement:

Conduct discovery workshops and design sessions to elicit AI &
- GenAI opportunities and client pain areas. Use advanced presentation, public speaking, and content creation skills for C-level discussions.

- Solution Architecture &

- Technology Landscape:

Assess impact to client's technology landscape/architecture; formulate guiding principles and platform components. Demonstrate knowledge of IT & enterprise architecture concepts — Cloud, API-first, MCP, agent orchestration, RAG, observability.

- Budgeting &

- Financial Proposals:

Manage budgeting and forecasting activities and build financial proposals for AI transformation engagements.

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Your Experience Counts!

- MBA from a tier 1 institute preferred.

- 3 – 5 years of Strategy Consulting experience at a consulting firm.

- 2+ years of experience in designing and building end-to-end Enterprise AI and GenAI strategic solutions using cloud and non-cloud platforms — Google Vertex AI / Gemini,



Azure AI / Copilot, Claude, Amazon Bedrock, IBM Watson, DataRobot, or equivalent.

- Excellent understanding of Traditional AI, Generative AI, Agentic AI, multi-agent orchestration, and Advanced Cognitive methods to derive insights and actions.

- Demonstrated ability to design agentic AI workflows, including LLM architecture, prompt engineering, workflow automation, and AI agent design for production environments.

- 2+ years of experience writing business cases (quantitative and qualitative) to support strategic business initiatives or AI &
- Data transformation, including value realisation and AI governance frameworks.

- Good working knowledge in formulation of relevant guiding principles and platform components with expertise in Responsible AI frameworks, including bias, explainability, and regulatory compliance.

- 2+ years of experience leading or managing large teams effectively including planning/structuring analytical work, facilitating team workshops, and developing AI strategy recommendations as well as developing POCs.

- Deep understanding of data supply chain and how it enables AI productionisation and scaled deployment.

- Mandatory knowledge of IT &

- Enterprise architecture concepts through practical experience — Cloud, API-first, RAG, MCP, agent orchestration, MLOps, observability.

- Robust understanding in any of the following industries preferred: Financial Services, Retail, Consumer Goods, Telecom, Life Sciences, Transportation, Automotive/Industrial, or equivalent.

- Cloud AI Practitioner Certifications (Azure, AWS, Google) desirable but not essential.

📌 Enterprise AI Value Strategy (Bengaluru)
🏢 SDNA Global
📍 Bengaluru