Role Summary

We are seeking a highly experienced Technical Presales Agentic AI Architect to lead customer engagements, solution design, and enterprise AI transformation initiatives focused on Agentic AI, Generative AI, Autonomous Systems, and AI Platforms.

This role combines deep technical architecture expertise with presales consulting responsibilities. The successful candidate will work closely with Sales, Customers, Product, Delivery, and Executive Leadership to shape AI opportunities, define enterprise architectures, build compelling solutions, and drive successful AI adoption.

You will be responsible for architecting enterprise-scale AI ecosystems that enable autonomous agents to understand business context, reason intelligently, execute actions, and continuously improve through feedback and learning.

Key Responsibilities

1. Technical Presales & Solution Consulting

- Lead customer discovery workshops and AI strategy discussions.
- Engage with C-Level executives, business leaders, and technology stakeholders.
- Translate business challenges into scalable Agentic AI and GenAI solutions.
- Develop technical proposals, solution architectures, implementation roadmaps, and Statements of Work (SOW).
- Support RFPs, RFIs, RFQs, and technical bid responses.
- Partner with Sales teams to build pursuit strategies and technical win plans.
- Present executive-level AI vision, business value, and architecture recommendations.

2. Agentic AI Architecture & Autonomous Systems

- Design and govern enterprise-scale Agentic AI platforms.
- Architect single-agent and multi-agent systems with planning, memory, reasoning, and execution capabilities.
- Define orchestration strategies for collaborative and hierarchical agent ecosystems.
- Enable agents to perform secure autonomous actions across enterprise applications and workflows.
- Design reusable AI platform services and agent frameworks.

3. Knowledge & Semantic Intelligence Layer

- Define enterprise knowledge ingestion and curation strategies.
- Architect RAG, knowledge graph, semantic search, and ontology-driven solutions.




- Build pipelines for structured and unstructured enterprise data.
- Establish metadata frameworks, contextual retrieval, and knowledge governance.
- Ensure AI systems have access to trusted, accurate, and continuously updated knowledge.

4. AI Decisioning & Cognitive Systems

- Design hybrid AI architectures combining:

- Large Language Models (LLMs)

- Machine Learning Models

- Rule Engines

- Deterministic Workflows
- Define model orchestration strategies across platforms and models.
- Implement reasoning, planning, and decision-making frameworks.
- Balance creativity, reliability, governance, and business risk.

5. Enterprise Integration & Action Frameworks

- Architect integrations with:

- ERP

- CRM

- HR Systems

- Data Platforms

- Enterprise APIs
- Design secure action frameworks enabling agents to execute business processes.
- Define event-driven and API-led integration architectures.
- Ensure enterprise-grade scalability, security, and reliability.

6. Governance, Security & Responsible AI

- Establish governance frameworks for autonomous AI systems.

- Implement
- Role-Based Access Controls (RBAC)
- Human-in-the-Loop workflows

- Auditability and Explainability

- Privacy and Compliance controls
- Define Responsible AI standards and operational guardrails.
- Ensure alignment with enterprise security and regulatory requirements.

7. AI Platform Engineering & Reusable Assets

- Build reusable:

- Agent Frameworks

- Reference Architectures

- Accelerators

- SDKs

- Solution Templates

- Demo Assets
- Create repeatable AI offerings that can be deployed across multiple customers.
- Support development of AI Centers of Excellence and platform strategies.

8. POCs, Demos & Innovation





- Lead design and execution of:

- Proof of Concepts (POCs)

- Pilot Programs

- Technical Demonstrations

- Innovation Workshops
- Create customer-facing demonstrations showcasing Agentic AI capabilities.
- Validate technical feasibility and business outcomes during sales cycles.
- Accelerate POC-to-production conversion.

Required Qualifications

Experience

- 15+ years of experience in Enterprise Architecture, Distributed Systems, AI/ML, Platform Engineering, or Solution Architecture.
- 5+ years of Technical Presales, Consulting, Solution Engineering, or Customer-Facing Architecture experience.
- Proven experience designing and delivering enterprise-scale AI/GenAI solutions.
- Experience supporting enterprise pursuits, proposals, and customer workshops.

Technical Expertise Solid hands-on experience with:

- Agentic AI Platforms
- Multi-Agent Systems
- Generative AI & LLMs
- RAG Architectures
- Vector Databases
- Knowledge Graphs
- AI Orchestration Frameworks
- Cloud Platforms (Azure, AWS, GCP)
- Databricks / Snowflake
- Python and AI Engineering Frameworks

Experience with:
- LangChain
- LlamaIndex
- Azure OpenAI
- OpenAI
- Anthropic
- MCP Frameworks
- Enterprise Integration Patterns

Critical Differentiators

- Successfully architected Agentic AI platforms beyond simple chatbot implementations.
- Strong balance of technical depth and executive communication skills.
- Proven track record supporting customer acquisitions through technical consulting and solution selling.
- Experience leading AI transformation programs from initial discovery through production deployment.
- Strong business acumen with the ability to connect AI investments to measurable business outcomes.

Success Metrics

- Technical win rate on qualified opportunities.
- POC-to-production conversion rate.
- Revenue influenced through AI solution consulting.
- Customer satisfaction and executive stakeholder feedback.
- Reusable AI assets, accelerators, and frameworks created.
- Successful deployment of enterprise-scale Agentic AI solutions.

📌 Presales – Agentic AI Architect (Bengaluru)
🏢 Computer Enterprises
📍 Bengaluru