Artificial Intelligence Architect

AI Architect

Experience: 815 Years
Role: AI Architect

Role Overview

We are seeking a visionary AI Architect with 815 years of experience to lead the strategic design and implementation of enterprise-scale AI solutions.

The ideal candidate should have strong expertise in Generative AI, Agentic AI, Responsible AI, AI Architecture, LLMs, RAG, and cloud-native AI platforms. The AI Architect will be responsible for defining AI architecture, assessing existing systems, establishing technology roadmaps, and guiding engineering teams in building scalable, secure, governed, and enterprise-ready AI solutions.

Key Responsibilities

1. Strategy & Roadmap – Optional

• Define and drive the AI strategy, aligning technology initiatives with business goals and innovation priorities.
• Develop and maintain the AI solution roadmap, covering short-term deliverables and long-term AI adoption.
• Evaluate emerging AI technologies, frameworks, models, and industry trends to support strategic decision-making.

2. Architecture & Design – Mandatory

• Design and architect end-to-end AI solutions using Generative AI, Agentic AI, LLMs, and Multimodal AI.
• Design intelligent agent systems using LangChain, LangGraph, Model Context Protocol (MCP), and Agent-to-Agent (A2A) protocols.
• Define scalable and modular architectures supporting RAG pipelines, Vector Databases, embeddings, and LLM-based applications.
• Define AI architecture standards, design patterns, and reusable components for enterprise adoption.
• Establish and enforce AI Governance and Responsible AI frameworks.
• Ensure AI solutions address Guardrails, AI ethics, security, privacy, compliance, and regulatory requirements.

3. Assessment & Optimization – Good to Have

• Conduct technical assessments of existing AI/ML systems, models, applications, and data pipelines.
• Identify architectural gaps, risks, performance issues, and opportunities for modernization.
• Recommend architectural improvements and integration strategies for legacy and enterprise systems.
• Evaluate AI models and solutions for performance, scalability, cost, security, and maintainability.

4. Deployment & Integration – Mandatory

• Lead deployment of AI/ML solutions using Docker, Kubernetes, and MLOps best practices.
• Integrate AI solutions with enterprise platforms and cloud-native AI services.
• Hands-on experience with at least one cloud platform: Azure, AWS, or GCP.
• Ensure AI solutions meet enterprise requirements for performance, scalability, reliability, security, and observability.
• Define deployment and operational strategies for production-grade AI applications.

5. Leadership & Collaboration – Good to Have

• Collaborate with Product Owners, Data Scientists, ML Engineers, Software Engineers, and Business Stakeholders.
• Mentor engineering teams and provide technical guidance across AI/ML initiatives.
• Drive architecture reviews and technical design discussions.
• Represent AI architecture in enterprise architecture, governance forums, and technical councils.

Mandatory Technical Skills

• Generative AI (GenAI)
• Agentic AI
• AI / Solution Architecture
• Python
• LLMs and Multimodal AI
• RAG and Embeddings
• Vector Databases
• Prompt Engineering
• Responsible AI
• AI Guardrails
• Docker and Kubernetes
• MLOps
• LangChain / LangGraph
• Model Context Protocol (MCP)
• Agent-to-Agent (A2A) Protocols
• Experience with at least one cloud-native AI platform: Azure, AWS, or GCP

AI Frameworks & Technologies

Experience with one or more of the following:

• LangChain
• LangGraph
• AutoGen
• CrewAI
• Model Context Protocol (MCP)
• Agent-to-Agent (A2A) Protocol
• RAG
• Fine-tuning
• Knowledge Bases / Vector Databases
• Embeddings
• Model Distillation
• Multimodal AI

Cloud-Native AI Services

Experience with ANY ONE of the following cloud platforms:

Azure AI

• Azure AI Foundry
• Azure AI Agents
• Azure AI Search
• Azure Bot Services

AWS AI

• Amazon Bedrock
• Amazon Q
• Amazon SageMaker

Google Cloud AI

• Vertex AI
• Model Garden
• Agentspace
• Agent Engine

AI Governance & Responsible AI

• Responsible AI principles and implementation
• AI Ethics and regulatory considerations
• AI Guardrails and safety mechanisms
• Data privacy and security
• Model governance and risk management
• Enterprise AI governance frameworks
• Compliance and responsible deployment of AI solutions