About the Role :

We are seeking a visionary and hands-on Head of AI Solutions & Agentic AI Architecture to lead the design, implementation, and scaling of enterprise AI platforms, Generative AI solutions, Agentic AI systems, and intelligent automation initiatives.

The ideal candidate combines deep expertise in AI architecture, enterprise technology, cloud platforms, and business transformation. This leader will work closely with executive stakeholders, product teams, engineering organizations, and customers to define AI strategy, architect scalable AI solutions, and drive measurable business outcomes.

This role requires both strategic leadership and technical depth across LLMs, Agentic AI, AI infrastructure, AI governance, and enterprise deployment patterns.

Key Responsibilities :

AI Strategy & Leadership :

- Define and execute enterprise AI and Agentic AI strategy.

- Establish AI architecture standards, governance frameworks, and operating models.

- Build and lead high-performing AI Architects, AI Engineers, Data Scientists, and AI Product teams.

- Partner with executive leadership to identify AI-led growth opportunities.

- Drive AI adoption across business functions and technology organizations.

AI Solution Architecture :

- Architect end-to-end AI solutions using LLMs, RAG, Multi-Agent Systems, and AI workflows.

- Design scalable AI platforms supporting enterprise-grade security, compliance, and governance.

- Define reference architectures for AI applications, copilots, autonomous agents, and intelligent automation.

- Guide customers and stakeholders through AI discovery, PoC, pilot, and production deployment phases.

Agentic AI & Autonomous Systems :

Design and deploy :

1. AI Agents

2. Multi-Agent Systems

3. Agent Orchestration Platforms

4. AI Workforce Solutions

5. Autonomous Business Process Automation

- Define agent memory, planning, reasoning, tool usage, workflow orchestration, and human-in-the-loop patterns.

- Establish frameworks for evaluating and monitoring agent performance.

Generative AI & LLM Engineering :

- Lead implementation of :

1. Retrieval-Augmented Generation (RAG)

2. Fine-Tuning

3. Prompt Engineering

4. AI Evaluation Frameworks

5. Model Routing

6. Knowledge Systems

- Evaluate and integrate leading foundation models from :

1. OpenAI

2. Anthropic

3. Google

4. Meta

5. Mistral

6. Cohere

AI Platform Engineering :

- Define AI platform roadmap including :

1. Model Serving

2. Vector Databases

3. MLOps

4. LLMOps

5. AIOps

6. Observability

- Architect cloud-native AI solutions across :

1. AWS

2. Azure

3. Google Cloud

Customer & Stakeholder Engagement :

- Act as executive AI advisor to customers and internal stakeholders.

- Conduct AI workshops, architecture reviews, and executive briefings.

- Translate business challenges into scalable AI solutions.

- Build trusted relationships with C-level executives and business leaders.

Innovation & Emerging Technology :

- Continuously evaluate emerging technologies including :

1. Agentic AI

2. Multimodal AI

3. AI Infrastructure

4. AI Governance

5. Autonomous Enterprise Systems

6. AI-native Operating Models

- Identify new business opportunities and innovation initiatives.

Required Qualifications :

Education :

Bachelor's or Master's Degree in :

1. Computer Science

2. Artificial Intelligence

3. Data Science

4. Engineering

5. Related technical disciplines

Experience :

- 12+ years in software engineering, architecture, AI, or enterprise technology.

- 5+ years leading AI/ML programs.

- Proven experience deploying AI solutions in production.

- Experience leading large-scale digital transformation initiatives.

- Experience engaging with executive stakeholders and enterprise customers.

Technical Skills :

AI & Machine Learning :

1. Generative AI

2. Large Language Models (LLMs)

3. Agentic AI

4. Multi-Agent Systems

5. RAG Architectures

6. Fine-Tuning

7. Prompt Engineering

8. AI Evaluation Frameworks

9. Knowledge Graphs

10. Vector Search

Programming :

1. Python

2. TypeScript

3. Java

4. REST APIs

AI Frameworks :

1. LangChain

2. LangGraph

3. LlamaIndex

4. CrewAI

5. AutoGen

6. Semantic Kernel

7. DSPy

Cloud Platforms :

1. AWS

2. Azure

3. Google Cloud

MLOps / LLMOps :

1. MLflow

2. Kubeflow

3. Databricks

4. Ray

5. Airflow

6. Argo Workflows

AI Infrastructure :

1. Kubernetes

2. Docker

3. GPU Infrastructure

4. NVIDIA Ecosystem

5. Model Serving Platforms

6. Vector Databases

Vector Databases :

1. Pinecone

2. Weaviate

3. Qdrant

4. Milvus

5. Chroma