AI Solution Architect

Futurism Technologies; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Futurism Technologies in Bengaluru seeks an experienced AI Architect to design and drive enterprise AI and Generative AI solutions. The role requires deep expertise in AI strategy, solution architecture, LLMs, GenAI platforms, vector databases, and both Low-Code/No-Code and Pro-Code approaches.

You will collaborate with business and technology teams to identify AI opportunities, define roadmaps, design scalable architectures, and lead the implementation of production-ready AI solutions.

Qualifications

• Experience in designing enterprise GenAI use cases and production solutions.
• Strong knowledge of LLMs, RAG, vector databases and AI agents.
• Ability to evaluate Low-Code/No-Code vs Pro-Code approaches.

Responsibilities

• Define and implement enterprise AI and Generative AI strategies aligned with business objectives.
• Design end-to-end AI/GenAI solution architectures including LLM applications, RAG, AI agents, data pipelines, and integrations.
• Lead production-grade AI delivery and collaborate with business and technology teams.

Skills

Generative AI

LLMs

Prompt engineering

Enterprise AI strategy

Architektur & design

Education

BTech / BE

BCA / MCA

Tools

Microsoft Copilot Studio

TrueFoundry

Codex / AI-assisted coding tools

APIs & microservices

Job description

Job Title: AI Architect

Location: Bengaluru

Work from office- 8 days per month

Payroll Futurism Technologies

Experience: 11+ Years

Qualification: BTech, BCA, MCA, BE

Looking for immediate joiner

Job Summary

We are looking for an experienced AI Architect to design and drive enterprise AI and Generative AI solutions. The ideal candidate should have strong experience in AI strategy, solution architecture, LLMs, GenAI platforms, vector databases, AI agents, and both Low-Code/No-Code and Pro-Code development approaches.

The candidate will work closely with business and technology teams to identify AI opportunities, define AI roadmaps, design scalable architectures, and lead the implementation of production-ready AI solutions.

Key Responsibilities

• Define and implement enterprise AI and Generative AI strategies aligned with business objectives.
• Design end-to-end AI/GenAI solution architectures, including LLM applications, RAG, AI agents, APIs, data pipelines, and integrations.
• Evaluate and recommend Low-Code/No-Code, Low-Code and Pro-Code/High-Code approaches based on business and technical requirements.
• Design AI solutions using platforms such as Microsoft Copilot Studio and other enterprise AI platforms.
• Work with OpenAI/GPT models and other LLMs, including model selection, prompt engineering, fine-tuning, evaluation, and optimization.
• Design and implement Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise data sources.
• Have hands-on understanding of Vector Databases, embeddings, semantic search, hybrid search, chunking, indexing, and retrieval strategies.
• Evaluate and work with modern AI development and deployment platforms such as TrueFoundry.
• Utilize Codex and AI-assisted coding/development tools to accelerate software and AI solution development.
• Design and govern AI Agent and Agentic AI architectures, including tool calling, workflows, orchestration, memory, and guardrails.
• Define architecture standards for security, scalability, reliability, cost optimization, observability, and responsible AI.
• Develop POCs, MVPs, and reference architectures and guide teams toward production implementation.
• Collaborate with engineering, data, cloud, security, and business teams to deliver enterprise AI solutions.
• Assess existing applications and identify opportunities for AI automation and modernization.
• Define AI governance, model evaluation, data privacy, access control, and responsible AI practices.
• Stay current with emerging LLM, GenAI, Agentic AI, AI platform, and AI engineering technologies.

Required Skills

AI & Generative AI

• Strong understanding of Generative AI, LLMs, RAG, AI Agents and Agentic AI.
• Hands-on experience with GPT models / OpenAI models and other LLMs.
• Strong knowledge of prompt engineering, embeddings,context management, model evaluation, and optimization.
• Experience designing enterprise GenAI use cases and production solutions.

AI Platforms & Development

• Experience with Microsoft Copilot Studio for building enterprise copilots and AI agents.
• Knowledge of TrueFoundry or similar AI/ML platform for model deployment and AI application lifecycle management.
• Experience with Codex / AI-assisted coding tools.
• Strong understanding of APIs, microservices, cloud platforms, and enterprise integration patterns.

Low-Code / No-Code & Pro-Code

• Strong understanding of Low-Code/No-Code platforms and their application in enterprise AI solutions.
• Ability to determine when to use Low-Code/No-Code versus Pro-Code/High-Code development.
• Strong programming/development understanding in languages such as Python, Java, JavaScript/TypeScript, or similar.

Vector Databases & RAG

• Experience with Vector Databases such as Pinecone, Azure AI Search, Weaviate, Milvus, Qdrant, or similar.
• Understanding of embeddings, vector search, semantic search, hybrid search, metadata filtering, chunking, and retrieval optimization.
• Experience designing scalable RAG architectures.

AI Strategy & Architecture

• Ability to create an enterprise AI strategy and roadmap.
• Experience conducting AI opportunity assessments and defining AI adoption roadmaps.
• Strong knowledge of enterprise architecture, cloud architecture, security, scalability, and integration.
• Ability to communicate complex AI concepts to both technical and business stakeholders.

Preferred Skills

• Experience with Azure OpenAI, Microsoft Azure AI services, AWS AI/ML services, or Google Cloud AI services.
• Experience with LangChain, LangGraph, Semantic Kernel, or similar AI orchestration frameworks.
• Knowledge of AI governance, responsible AI, model security, data privacy, and AI observability.
• Experience with MLOps/LLMOps and CI/CD for AI applications.
• Experience implementing enterprise AI solutions at scale.