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Job Overview
We are looking for a Senior AI/GenAI Engineer with strong hands-on experience building and deploying production-grade AI solutions on Azure. This is not a data engineering role. This role is responsible for designing end-to-end AI architectures, leading technical decisions, and translating business needs into scalable, reliable, and secure GenAI systems.
You will work closely with cross-functional teams to deliver AI-powered solutions (copilots, AI agents, RAG pipelines) that support both internal operations and client-facing services.
Key Responsibilities
Design, build, and deploy production-grade GenAI solutions (AI agents, copilots, RAG pipelines) with a focus on scalability, reliability, and performance
Own end-to-end architecture of AI systems on Azure, including integration with enterprise data platforms and backend services
Lead the development of LLM-powered applications using Azure-native services and modern orchestration frameworks
Implement LLMOps practices, including monitoring, logging, evaluation, prompt/version management, and cost optimization
Translate business requirements into technical AI solutions and define scalable implementation strategies
Ensure security, governance, and compliance across all AI solutions
Optimize AI systems for latency, cost, and accuracy in production environments
Provide technical leadership, mentor engineers, and drive best practices in system design and code quality
Required Qualifications
Proven experience as a Senior or Lead Engineer delivering production-grade AI/ML or GenAI solutions
Strong hands-on expertise in Azure, including designing and deploying end-to-end AI architectures
Experience with Azure AI Foundry, Azure OpenAI, and enterprise AI service integration
Solid experience building AI agents using Microsoft Agent Framework (or similar frameworks)
Strong Python experience building AI applications, APIs, and LLM-based services (not only data pipelines)
Strong experience with RAG architectures, prompt engineering, and LLM orchestration
Experience with Databricks, Azure Data Factory, and SQL for data-driven AI solutions