AI/ML Architect

Title: AI Architect

Years of Exp: 10+ Years

Location: All Ascendion Locations

Number of POS: 1

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.