Job description

About the Role

We are looking for a Senior AI Engineer to design, build, and operate production-grade Generative AI solutions on the Microsoft Azure AI ecosystem. You will be the technical anchor for our GenAI initiatives, owning the end-to-end lifecycle: from foundation model selection and prompt/RAG architecture through deployment, MLOps, security hardening, and continuous evaluation. This is a hands-on senior role. You will set technical direction, mentor engineers, and work directly with product, data, security, and platform teams to move AI use cases from prototype to reliable, governed, cost-efficient services.

What You Will do

GenAI solution design and delivery

• Architect and build LLM-powered applications (RAG, agents, copilots, document intelligence, content understanding, conversational systems) using Azure AI Foundry, Azure OpenAI, and the broader Azure AI and data portfolio

. • Design retrieval pipelines with Azure AI Search (vector, hybrid, and semantic ranking), including chunking, embedding, indexing, and relevance tuning strategies.

• Evaluate, fine-tune, and deploy foundation and open-source models (e.g., GPT, Phi, Llama, Mistral, Kimi, GLM) through Azure AI Foundry model catalog and Azure Machine Learning.

• Implement prompt engineering, orchestration frameworks (Semantic Kernel, LangChain, Prompt Flow, or equivalent), and structured evaluation of model quality, groundedness, and safety.

Platform, MLOps, and productionization

• Build and maintain MLOps/LLMOps pipelines on Azure ML, Github Enterprise: experiment tracking, model registry, CI/CD for models and prompts, automated evaluation, monitoring, and drift/cost management.

• Expose AI capabilities as scalable microservices (Azure Kubernetes Service, Azure Container Apps, Azure Functions, API Management), with attention to latency, throughput, resilience, and cost.

• Establish observability for AI systems: tracing, token/cost telemetry, quality metrics, and feedback loops.

AI security and governance

• Apply Responsible AI and AI security practices: content safety filters, prompt-injection and jailbreak mitigation, data-leakage controls, PII handling, and red-teaming.

• Implement secure architectures using Azure identity (Entra ID, managed identities), private endpoints, Key Vault, network isolation, and data residency controls.

• Contribute to AI governance standards, model risk documentation, and compliance requirements.

Technical leadership

• Define reference architectures, coding standards, and reusable components for GenAI workloads.

• Mentor and review the work of other engineers; lead design discussions and technical decision-making.

• Partner with stakeholders to translate business problems into feasible, measurable AI solutions and communicate trade-offs clearly.

• Stay current with the rapidly evolving model and tooling landscape and bring practical recommendations to the team.