Artificial Intelligence Architect

Spectrum Talent Management

Pune District, Chennai District, Bengaluru

On-site

INR 3,000,000 - 5,400,000

Full time

2 days ago

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Job summary

Spectrum Talent Management is seeking an AI Architect to lead strategic AI design for enterprise-scale solutions. You will define AI architecture, assess systems, and guide engineering teams in scalable, governed AI deployments.

Responsibilities include strategy & roadmaps, end-to-end design with GenAI, LLMs, RAG, and MCP protocols, plus hands-on deployment using Docker, Kubernetes, and major cloud platforms. Strong governance, security, and compliance focus is required.

Qualifications

Must have expertise in building enterprise-scale AI architectures and governance frameworks.
Experience designing end-to-end AI solutions with attention to security, privacy, and compliance.
Proficiency with Docker, Kubernetes, and MLOps best practices.

Responsibilities

Define and drive the AI strategy and roadmap aligned with business goals.
Design end-to-end AI solutions using GenAI, LLMs, and Multimodal AI.
Lead deployment and integration using cloud-native platforms and MLOps.

Skills

Generative AI

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

Cloud-native AI platform (Azure/AWS/GG

Tools

LangChain

LangGraph

MCP

A2A Protocol

RAG

Embeddings

Vector Databases

Model Distillation

Knowledge Bases

Job description

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