Senior Enterprise AI Solutions Architect

16 hours ago

Anantapur, Andhra Pradesh, India beBeeArtificialIntelligence Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Azure AI Engineer Job Description

We are seeking an experienced and highly skilled AI engineer to design, configure, and optimize enterprise-grade AI solutions using Microsoft Azure AI services.

  • Key Responsibilities:
  • Design and develop scalable AI solutions using Azure AI services.
  • Architect and implement RAG-based applications and intelligent agents for search, document processing, and other NLP/NLU use cases.
  • Write robust, maintainable Python backend code to integrate and orchestrate Azure AI services.
  • Optimize performance, cost, and scalability of deployed AI workloads.
  • Develop APIs and service layers to enable AI features across products and platforms.
  • Collaborate with data scientists, DevOps, and product teams to deploy AI models and pipelines.
  • Monitor, troubleshoot, and maintain AI-driven features in production environments.
  • Stay current with Azure's evolving AI stack and emerging best practices in RAG and GenAI.

Requirements:

  • Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, or a related field.
  • 7+ years of experience in AI/ML or backend software engineering.
  • Strong hands-on experience with Microsoft Azure AI services.
  • Proven experience in building and deploying RAG (Retrieval-Augmented Generation) applications.
  • Proficient in Python, including building RESTful APIs, microservices, and data processing pipelines.
  • Familiarity with vector databases, embedding models, and semantic search.
  • Experience with Azure AI SDKs, REST APIs, and security/authentication mechanisms.
  • Strong understanding of NLP, LLMs, prompt engineering, and AI agent design.

Preferred Qualifications:

  • Experience integrating Azure AI services into enterprise workflows or internal tools.
  • Knowledge of LangChain, Semantic Kernel, or similar LLM frameworks.
  • Familiarity with CI/CD pipelines and infrastructure-as-code on Azure.
  • Exposure to MLOps and model lifecycle management on Azure ML.
  • Experience working in agile development environments.

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