TestUnity
Website:
testunity.com
Job details:
About The Role
We are looking for an experienced Azure Architect with deep expertise in Retrieval-Augmented Generation (RAG), Generative AI, and enterprise-scale cloud architecture. The ideal candidate will design and implement scalable AI solutions on Microsoft Azure using Azure OpenAI and modern AI orchestration frameworks. This role requires strong hands-on architecture experience in building secure, production-grade GenAI applications integrated with enterprise systems and data platforms.
Key Responsibilities
- Design, architect, and implement Azure-based AI/ML and Generative AI solutions with strong expertise in RAG architecture.
- Build scalable and secure RAG pipelines using Azure OpenAI, Azure AI Search, Vector Databases, Cognitive Services, and frameworks such as LangChain and LlamaIndex.
- Define enterprise-grade architecture for: ○ Document ingestion ○ Data chunking ○ Embedding generation ○ Vector indexing ○ Semantic search ○ LLM orchestration
- Integrate AI solutions with enterprise applications, APIs, databases, SharePoint, and cloud/native data sources.
- Lead end-to-end solution architecture covering: ○ Security and governance ○ Performance optimization ○ Cost management ○ Scalability and reliability
- Collaborate with business stakeholders, product teams, data engineers, and developers to translate business use cases into AI-driven solutions.
- Implement prompt engineering strategies, grounding techniques, hallucination mitigation, and AI observability for enterprise GenAI applications.
- Provide technical leadership, architecture governance, best practices, and mentoring to engineering teams.
- Design CI/CD pipelines and cloud-native deployment strategies for AI workloads.
- Drive architecture reviews, technology evaluations, and proof-of-concept initiatives. Required Skills & Qualifications
Technical Skills
- 9+ years of experience in software engineering, cloud architecture, or enterprise solution architecture.
- Strong expertise in Microsoft Azure cloud ecosystem.
- Hands-on experience with: ○ Azure OpenAI Service ○ Azure AI Studio ○ Azure AI Search ○ Azure Functions ○ Azure Kubernetes Service (AKS) ○ Azure Cosmos DB ○ Azure Data Factory ○ Azure API Management
- Strong understanding of RAG architecture and vector search implementations.
- Experience with vector databases and embedding models.
- Proficiency in Python and AI orchestration frameworks such as LangChain or LlamaIndex.
- Experience with REST APIs, microservices, and cloud-native architectures.
- Knowledge of enterprise security, IAM, networking, and governance in Azure.
- Understanding of DevOps, CI/CD pipelines, Infrastructure as Code (Terraform/Bicep), and containerization.
Preferred Skills
- Experience with enterprise document processing and knowledge management systems.
- Familiarity with AI observability and monitoring tools.
- Exposure to multi-agent AI systems and autonomous workflows.
- Microsoft Azure certifications are preferred.
- Experience working in Agile/Scrum environments.
Soft Skills
- Strong problem-solving and analytical capabilities.
- Excellent communication and stakeholder management skills.
- Ability to lead architecture discussions and mentor technical teams.
- Strong collaboration skills across business and technology teams.
Nice to Have
- Experience with hybrid cloud or multi-cloud AI deployments.
- Exposure to MLOps and Responsible AI practices.
- Knowledge of compliance frameworks and enterprise governance standards.
What We Offer
- Opportunity to work on cutting-edge Generative AI and enterprise RAG platforms.
- Flexible remote working environment.
- Exposure to large-scale enterprise AI transformation programs.
- Collaborative and innovation-driven culture.
- Career growth and leadership opportunities.Tips: Provide a summary of the role, what success in the position looks like, and how this role fits into the organization overall.
Responsibilities
[Be specific when describing each of the responsibilities. Use gender-neutral, inclusive language.]
Example: Determine and develop user requirements for systems in production, to ensure maximum usability
Qualifications
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Example: Excellent verbal and written communication skills
Skills: architecture,data,azure
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