AI/ML Architect

Ascendion; Bengaluru; Hybrid; INR 3,500,000 - 6,500,000; Full time

Job summary

Ascendion is seeking an experienced AI Architect to lead the design and implementation of cloud-native AI platforms on Microsoft Azure. You will shape scalable, secure architectures for enterprise AI applications and mentor engineering teams.

The role covers agentic AI, LLM integrations, RAG pipelines, IaC, CI/CD, and governance, with a strong emphasis on performance, reliability, and cost optimization across all Ascendion locations.

Qualifications

• 10+ years of experience in software engineering, AI/ML, cloud architecture for enterprise AI.
• Hands-on expertise in Microsoft Azure and Azure-native AI services.
• Strong knowledge of Generative AI, LLMs, embeddings, vector search, RAG, and agentic AI.
• Experience with Azure OpenAI and enterprise LLM integrations.
• Strong Python programming experience.
• Experience designing multi-agent workflows and AI orchestration.
• Knowledge of API design, microservices, authentication, and enterprise integration.
• IaC experience with Terraform and/or Azure Bicep.
• Understanding DevOps, CI/CD, containerization, and cloud security.
• Experience with AI observability and production operations.
• Understanding of AI security, data privacy, responsible AI, compliance, and governance.

Responsibilities

• Architect and implement cloud-native AI platforms on Azure.
• Design scalable, secure, production-ready architectures for enterprise AI applications.
• Design and implement agentic AI and multi-agent architectures, including orchestration and memory.
• Integrate LLMs including Azure OpenAI and embeddings.
• Build enterprise-grade RAG pipelines using Azure AI Search and vector databases.
• Evaluate AI protocols like MCP and A2A and adopt where appropriate.
• Design AI orchestration solutions using LangGraph, Semantic Kernel, AutoGen, or equivalents.
• Develop secure and scalable APIs/services with authentication and secrets management.
• Leverage Azure services like Azure Functions, App Services, API Management, Key Vault, Service Bus.
• Define and implement IaC with Terraform or Azure Bicep and establish CI/CD practices.
• Implement observability: logging, tracing, metrics, model performance, cost, AI quality.
• Ensure AI systems adhere to security, privacy, compliance, governance.

Skills

Azure

GenAI

LLMs

Python

Multi-agent workflows

Terraform

Azure Bicep

DevOps

Security

Education

Bachelor's or Master's in CS/AI

Tools

LangGraph

Semantic Kernel

AutoGen

LangChain

Azure OpenAI

Vector search

Job description

Title: AI Architect

Years of Exp: 10+ Years

Location: All Ascendion Locations

Number of POS: 1

• Architect and implement cloud-native AI platforms and solutions using Microsoft Azure.
• Design scalable, secure, highly available, and production-ready architectures for enterprise AI applications.
• Design and implement agentic AI and multi-agent architectures, including agent orchestration, workflow management, tool invocation, state management, and memory.
• Design and implement integrations with Large Language Models (LLMs), including Azure OpenAI, embeddings, fine-tuned models, and other foundation models.
• Build enterprise-grade Retrieval-Augmented Generation (RAG) pipelines using Azure AI Search, vector databases, document stores, and other Azure services.
• Evaluate and implement emerging AI protocols and standards such as MCP (Model Context Protocol) and A2A (Agent2Agent).
• Design and implement AI orchestration solutions using frameworks such as LangGraph, Microsoft Semantic Kernel, AutoGen, or equivalent technologies.
• Develop secure and scalable APIs and services for AI applications, including authentication, authorization, identity, and secrets management.
• Leverage Azure services such as Azure Functions, App Services, Azure AI Search, Azure OpenAI, Document Intelligence, API Management, Key Vault, Service Bus, and managed identities.
• Define and implement Infrastructure as Code (IaC) using technologies such as Terraform or Azure Bicep.
• Establish CI/CD and DevOps practices for AI applications, infrastructure, models, and supporting services.
• Implement comprehensive observability and monitoring, including logging, distributed tracing, metrics, model performance, latency, cost, and AI quality monitoring.
• Design AI systems with appropriate security, privacy, compliance, responsible AI, and data governance controls.
• Establish best practices for prompt management, AI evaluation, guardrails, model selection, and production readiness.
• Collaborate with software engineers, data engineers, ML engineers, DevOps teams, security teams, architects, and business stakeholders.
• Conduct technical evaluations and proof-of-concepts for emerging AI technologies and frameworks.
• Provide technical leadership, architecture guidance, and mentoring to engineering teams.
• Ensure AI solutions are designed for scalability, reliability, maintainability, performance, and cost optimization.

Key Responsibilities

• Architect and implement cloud-native AI platforms and solutions using Microsoft Azure.
• Design scalable, secure, highly available, and production-ready architectures for enterprise AI applications.
• Design and implement agentic AI and multi-agent architectures, including agent orchestration, workflow management, tool invocation, state management, and memory.
• Design and implement integrations with Large Language Models (LLMs), including Azure OpenAI, embeddings, fine-tuned models, and other foundation models.
• Build enterprise-grade Retrieval-Augmented Generation (RAG) pipelines using Azure AI Search, vector databases, document stores, and other Azure services.
• Evaluate and implement emerging AI protocols and standards such as MCP (Model Context Protocol) and A2A (Agent2Agent).
• Design and implement AI orchestration solutions using frameworks such as LangGraph, Microsoft Semantic Kernel, AutoGen, or equivalent technologies.
• Develop secure and scalable APIs and services for AI applications, including authentication, authorization, identity, and secrets management.
• Leverage Azure services such as Azure Functions, App Services, Azure AI Search, Azure OpenAI, Document Intelligence, API Management, Key Vault, Service Bus, and managed identities.
• Define and implement Infrastructure as Code (IaC) using technologies such as Terraform or Azure Bicep.
• Establish CI/CD and DevOps practices for AI applications, infrastructure, models, and supporting services.
• Implement comprehensive observability and monitoring, including logging, distributed tracing, metrics, model performance, latency, cost, and AI quality monitoring.
• Design AI systems with appropriate security, privacy, compliance, responsible AI, and data governance controls.
• Establish best practices for prompt management, AI evaluation, guardrails, model selection, and production readiness.
• Collaborate with software engineers, data engineers, ML engineers, DevOps teams, security teams, architects, and business stakeholders.
• Conduct technical evaluations and proof-of-concepts for emerging AI technologies and frameworks.
• Provide technical leadership, architecture guidance, and mentoring to engineering teams.
• Ensure AI solutions are designed for scalability, reliability, maintainability, performance, and cost optimization.

Required Skills & Experience

• 10+ years of experience in software engineering, AI/ML, cloud architecture, or related technology domains, with significant experience delivering enterprise AI/GenAI solutions.
• Strong hands-on expertise in Microsoft Azure and Azure-native AI services.
• Strong understanding of Generative AI, LLMs, prompt engineering, embeddings, vector search, RAG, and agentic AI architectures.
• Hands-on experience with Azure OpenAI and enterprise LLM integrations.
• Strong programming experience in Python.
• Experience designing and implementing multi-agent workflows, AI agents, tool calling, state management, and orchestration.
• Experience with one or more agent/AI frameworks such as LangGraph, Semantic Kernel, AutoGen, LangChain, or equivalent.
• Strong knowledge of Azure AI Search, vector databases, document processing, and cloud storage.
• Experience with MCP, A2A, or other emerging AI interoperability protocols.
• Strong understanding of API design, microservices, authentication, authorization, and enterprise integration patterns.
• Experience with Infrastructure as Code, preferably Terraform and/or Azure Bicep.
• Strong understanding of DevOps, CI/CD, containerization, and cloud security.
• Experience with AI/ML observability, monitoring, evaluation, and production operations.
• Understanding of AI security, data privacy, responsible AI, compliance, and governance.
• Strong architecture, analytical, communication, and problem-solving skills.

Education Qualification

• Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Machine Learning, Engineering, or a related technical discipline.
• Equivalent practical experience in AI, cloud architecture, software engineering, or related fields may be considered.