Senior Azure OpenAI Engineer

ThreatXIntel; Chennai District; On-site; INR 4,500,000 - 6,500,000; Full time

Job summary

ThreatXIntel is seeking a Senior Azure OpenAI Engineer with 5–8 years of software development experience to design, deploy, and operate secure enterprise Generative AI solutions using Azure OpenAI, Azure AI Foundry, and related services.

You will lead implementation of RAG, AI agents, and copilot-style apps, integrate with enterprise platforms, and mentor engineers while ensuring scalable, testable, and compliant production systems.

Qualifications

• 5+ years of AI and IT/software engineering experience.
• 3+ years hands-on application development.
• 2+ years practical Azure OpenAI project experience.
• Strong Python, API, REST, and async programming skills.
• Experience with Azure AI services, embeddings, and RAG workflows.

Responsibilities

• Design and develop enterprise AI apps using Azure OpenAI Service and Azure AI Foundry.
• Build Copilot-style applications, intelligent search, and document automation platforms.
• Implement RAG solutions with embeddings, vector search, and grounding.
• Develop AI workflows with tool-calling, orchestration, and human-in-the-loop controls.
• Design scalable backends and microservices using Python, FastAPI/Flask, REST APIs, and CI/CD.

Skills

Python

Azure OpenAI

APIs

DevOps

AI Architecture

Microservices

Education

Bachelor's degree in Computer Science

Tools

Azure AI Foundry

Azure Functions

Azure SQL

Cosmos DB

Docker

Kubernetes

Job description

Job Description:-

We are looking for a Senior Azure OpenAI Engineer with 5 8 years of software development experience to develop, deploy, and operate secure enterprise Generative AI solutions. The role requires strong hands-on expertise in Python, APIs, cloud-native engineering, Retrieval-Augmented Generation (RAG), AI agents, Azure services, application integration, DevOps, and technical leadership.

What You'll Do:-

1. Design and develop enterprise-grade AI applications using Azure OpenAI Service and Azure AI Foundry.
2. Build Copilot-style applications, conversational assistants, intelligent search solutions, and document automation platforms.
3. Design and implement RAG solutions using embeddings, vector search, semantic ranking, hybrid retrieval, grounding, and citations.
4. Develop agentic AI workflows, tool-calling patterns, orchestration logic, and human-in-the-loop controls.
5. Design scalable backend services and microservices using Python, FastAPI or Flask, REST APIs, asynchronous processing, and established software engineering practices.
6. Integrate AI capabilities with web applications, Microsoft Teams, SharePoint, enterprise platforms, ERP systems, databases, and internal APIs.
7. Apply clean architecture, design patterns, automated testing, code quality controls, reusable components, and maintainable documentation.
8. Design and deploy solutions using Azure OpenAI, Azure AI Search, Azure Functions, Azure App Service, Azure Storage, Azure Key Vault, Azure SQL, Cosmos DB, Azure Container Apps, or AKS as appropriate.
9. Implement secure access using Microsoft Entra ID, managed identities, RBAC, private networking, secrets management, and enterprise security controls.
10. Engineer resilient production solutions with monitoring, logging, tracing, exception handling, scalability, availability, and disaster-recovery considerations.
11. Design prompts, system instructions, reusable templates, structured outputs, function calling, and evaluation datasets.
12. Measure and improve answer relevance, groundedness, retrieval quality, latency, token consumption, reliability, and cost.
13. Implement guardrails, content filters, privacy controls, Responsible AI practices, hallucination mitigation, and auditability.
14. Build CI/CD pipelines using Azure DevOps or GitHub, including automated testing, security scanning, release controls, and environment promotion.
15. Containerize applications using Docker and deploy to managed Azure compute or Kubernetes platforms.
16. Monitor production usage, model behavior, application health, security events, and operational costs; lead troubleshooting and root-cause analysis.
17. Lead solution design, technical estimation, architecture reviews, code reviews, engineering standards, and reusable reference patterns.
18. Mentor AI engineers and developers while collaborating with data engineers, data scientists, MLOps engineers, security teams, infrastructure teams, and business stakeholders.
19. Translate business requirements into secure, maintainable, measurable AI products and guide delivery from proof of concept to production.

What We Seek In You

1. 5+ years of in AI and overall IT / software engineering experience.
2. 3+ years of hands-on application development experience.
3. 3+ years of Python and API development preferred.
4. 2 years of Microsoft Azure cloud engineering preferred.
5. 2 years of practical Azure OpenAI project experience preferred.
6. Demonstrated production or near-production implementation experience with RAG / enterprise knowledge solutions.
7. Strong hands-on expertise in Python, SQL, REST APIs, JSON, asynchronous programming, object-oriented design, and unit testing.
8. Strong hands-on expertise in Azure OpenAI, GPT models, embeddings, prompt engineering, structured output, function calling, and AI agents.
9. Hands-on experience with Azure AI Search, vector indexing, semantic and hybrid search, chunking, retrieval, reranking, and grounding.
10. Hands-on experience with Semantic Kernel, Lang Chain, Llama Index, AutoGen or comparable orchestration frameworks.Strong Azure platform experience across Azure AI Foundry, Functions, App Service, Storage, Key Vault, SQL, Cosmos DB, Monitor, Container Apps / AKS.
11. Strong understanding of microservices, event-driven design, API security, integration patterns, scalability, and resiliency.
12. Experience with Git, Azure DevOps or GitHub, CI/CD, Docker, and Kubernetes; Infrastructure as Code preferred.
13. Experience with Microsoft Entra ID, RBAC, managed identity, network isolation, secrets, Responsible AI, and audit controls.
14. Hands-on experience with Application Insights, Azure Monitor, logging, tracing, quality evaluation, usage and cost monitoring.
15. Strong problem-solving skills and the ability to balance business value, engineering quality, security, cost, and delivery speed.
16. Strong communication skills with the ability to explain complex AI architecture and risks to technical and non-technical stakeholders.
17. Practical ownership mindset, with evidence of taking AI solutions from design through production operations.

Preferred Qualifications

1. Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related discipline.
2. Microsoft certification in Azure AI, Azure development, Azure architecture, data engineering, or DevOps.
3. Experience delivering enterprise Copilot, intelligent document processing, IT automation, or knowledge management solutions.
4. Exposure to manufacturing, automotive, supply chain, quality, finance, or enterprise IT use cases.
5. Experience with Infrastructure as Code, model evaluation frameworks, AI red teaming, or production LLM observability.

Relevant Project Experience

1. Enterprise Knowledge Copilot - Secure RAG-based assistant that answers from governed enterprise content with citations and access control.
2. Intelligent Service or Ticket Assistant - AI integrated with enterprise service data to classify, summarize, retrieve knowledge, recommend actions, or accelerate resolution.
3. Document Intelligence and Automation - AI-powered processing of documents or messages to extract, summarize, validate, route, and generate actionable insights.
4. Production AI Platform - Deployment pipelines, evaluation, monitoring, security controls, observability, scaling, and cost management for LLM applications.