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 – Valuable 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

📌 Artificial Intelligence Architect (Pune)
🏢 Spectrum Talent Management
📍 Pune