
Senior Cloud AI Specialist
2 weeks ago
Unlock Azure AI Expertise with a Career-Changing Opportunity.
We are seeking an experienced and highly skilled professional to join our team as a full-time Azure AI Engineer. As a key member of our organization, you will be responsible for designing, configuring, and optimizing enterprise-grade Azure AI solutions.
- Design and develop scalable Azure AI solutions using Azure AI services, including:
- Azure OpenAI
- Azure Cognitive Search
- Azure Speech Services (speech-to-text, text-to-speech)
- Azure Document Intelligence (formerly Form Recognizer)
- 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.
- 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.
- Excellent problem-solving, collaboration, and communication skills.
- 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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