The Role
Designs and leads enterprise AI architectures integrating LLMs, NLP, agentic frameworks, front-end and back-end systems. Responsibilities include selecting AI frameworks, governing data flows and orchestration, integrating APIs and cloud platforms, ensuring security and scalability, and guiding deployment across on-premises, cloud, and hybrid environments. The role also mentors engineering teams and leads AI modernization and automation initiatives.
Summary Generated by Built In
Job Title: AI Architect
Experience: 10–18 Years
Location: Hyderabad (Onsite / Hybrid)
Company: AidenAI
About AidenAI
AidenAI is a next-generation AI and digital transformation company that enables enterprises to modernize, automate, and accelerate through intelligent systems. Our platform ecosystem — including AiDAP (AI-driven modernization), HX (Human Experience), TX (Testing), and Industry AI Agents — integrates deep domain knowledge with agentic AI to drive measurable impact across industries like insurance, financial services, and healthcare.
We are looking for an AI Architect who combines strong application architecture expertise with hands-on experience in AI frameworks, LLMs, and intelligent solution design.
Role Overview
As an AI Architect at AidenAI, you will be responsible for designing, integrating, and delivering enterprise-grade AI solutions that combine front-end, back-end, and AI components into cohesive, production-ready systems. You will work across architectural frameworks (MCP, MVC, microservices, agentic AI) and drive scalable, modular designs that power AidenAI’s next-generation products and client solutions.
Key Responsibilities
Lead the end-to-end architecture and design of AI-driven enterprise applications and platforms.
Define and implement scalable AI solution architectures integrating LLMs, NLP, and agentic frameworks with front-end and back-end systems.
Evaluate and select appropriate AI frameworks (e.g., Autogen, LangChain, MCP) and orchestrate their integration with existing enterprise ecosystems.
Collaborate with engineering, AI, and product teams to translate business use cases into robust, deployable architectures.
Design and govern data flow, orchestration, and component interaction across AI models, APIs, and services.
Ensure architectural compliance with security, scalability, and performance best practices.
Evaluate and recommend suitable deployment models — including on-prem, cloud, and hybrid setups.
Mentor development and AI engineering teams on architectural standards, coding practices, and AI model integration.
Stay abreast of evolving AI architecture patterns, frameworks, and hyperscaler innovations.
Required Skills & Experience
Architecture Expertise:
Proven experience designing and implementing end-to-end enterprise architectures across multiple frameworks (e.g., MCP, MVC, microservices).
Deep understanding of solution integration patterns connecting front-end, back-end, and AI layers.
AI Frameworks & Technologies:
Hands-on experience with Python, LLMs, NLP, Agentic AI, Autogen, MCP, and related ecosystems.
Exposure to custom AI stacks as well as hyperscaler AI platforms (e.g., Hugging Face, Azure AI Foundry, AWS Bedrock, Vertex AI).
Understanding of prompt engineering, model orchestration, and multi-agent frameworks.
Integration & Delivery:
Strong understanding of APIs, data orchestration, and event-driven architectures.
Experience integrating AI services with front-end (React, Angular) and backend (Java, .NET, Node.js) applications.
Cloud & DevOps Knowledge:
Proficiency in cloud-native architectures (AWS, Azure, GCP).
Exposure to CI/CD, containerization (Docker, Kubernetes), and deployment pipelines.
Preferred Attributes
Experience leading AI modernization or agentic automation projects.
Knowledge of vector databases, embeddings, and retrieval frameworks (RAG).
Ability to design and govern AI lifecycle management — from data prep to deployment and monitoring.
Strong technical communication, leadership, and stakeholder management skills.
Education
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Certifications in AI/ML, cloud architecture, or enterprise solution design are an advantage.
Why Join AidenAI
Architect next-generation AI platforms integrating cognitive intelligence and automation.
Work with cutting-edge technologies like LLMs, Autogen, and agentic AI frameworks.
Collaborate with global clients and innovation teams across diverse industry verticals.
Be part of a fast-scaling, AI-first organization shaping the future of digital transformation.
Skills Required
- 10-18 years of professional experience
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- Experience designing and implementing end-to-end enterprise architectures
- Experience with MCP, MVC, and microservices architectures
- Strong understanding of solution integration patterns across front-end, back-end, and AI layers
- Hands-on experience with Python, LLMs, NLP, agentic AI, Autogen, and MCP
- Experience with AI platforms or ecosystems such as Hugging Face, Azure AI Foundry, AWS Bedrock, or Vertex AI
- Understanding of prompt engineering, model orchestration, and multi-agent frameworks
- Strong understanding of APIs, data orchestration, and event-driven architectures
- Experience integrating AI services with React or Angular front ends and Java, .NET, or Node.js back ends
- Proficiency in cloud-native architectures across AWS, Azure, or GCP
- Exposure to CI/CD, Docker, Kubernetes, and deployment pipelines
- Experience leading AI modernization or agentic automation projects
- Knowledge of vector databases, embeddings, and retrieval frameworks such as RAG
- Ability to design and govern AI lifecycle management from data preparation through monitoring
- Certifications in AI/ML, cloud architecture, or enterprise solution design
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The Company
What We Do
AidenAI builds and operates enterprise AI solutions, including AI agents and its unified agentic platform for modernizing legacy technology, transforming business logic and data, validating outcomes, and accelerating migration. It supports organizations from AI strategy through integration and ongoing operations, while providing tools to build, deploy, govern, and measure enterprise agents. Its services are intended to move AI beyond experimentation and embed it into business operations.
