About the Role We are looking for a Senior AI Engineer to design build and operate productiongrade Generative AI solutions on the Microsoft Azure AI ecosystem You will be the technical anchor for our GenAI initiatives owning the endtoend lifecycle from foundation model selection and promptRAG architecture through deployment MLOps security hardening and continuous evaluation This is a handson 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 costefficient services What You Will do GenAI solution design and delivery Architect and build LLMpowered 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 finetune and deploy foundation and opensource models eg 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 safetyPlatform MLOps and productionization Build and maintain MLOpsLLMOps pipelines on Azure ML Github Enterprise experiment tracking model registry CICD for models and prompts automated evaluation monitoring and driftcost 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 tokencost telemetry quality metrics and feedback loops AI security and governance Apply Responsible AI and AI security practices content safety filters promptinjection and jailbreak mitigation dataleakage controls PII handling and redteaming 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 decisionmaking Partner with stakeholders to translate business problems into feasible measurable AI solutions and communicate tradeoffs clearly Stay current with the rapidly evolving model and tooling landscape and bring practical recommendations to the teamRequirementsRequired Qualifications 810 years of overall experience in AI machine learning and data engineering with a strong track record of delivering production systems Minimum 23 years of handson experience with Azure AI Foundry formerly Azure AI Studio and Azure OpenAI Service including deploying and operating GenAI applications in production Deep expertise across the Azure AI ecosystem Azure Machine Learning Azure AI Search Azure AI Services Document Intelligence Language Speech Vision Fabric and Azure AI Content Safety Strong understanding of LLMs and foundation models architectures tokenization context management embeddings finetuning LoRAPEFT quantization and evaluation methods Practical experience with opensource models and frameworks Hugging Face Llama Mistral Phi vLLMONNX Runtime or similar serving stacks Proven MLOpsLLMOps experience CICD model versioning automated testing and evaluation monitoring and rollback strategies Experience designing and building microservices and APIs containers Kubernetes RESTgRPC eventdriven patterns with Azure DevOps or GitHub Actions Demonstrated knowledge of AI security and Responsible AI threat modeling for LLM applications OWASP Top 10 for LLMs data protection access control and safety guardrails Working experience with at least one other cloud provider AWS or GCP and their AIML services eg Amazon Bedrock SageMaker Vertex AI Expertlevel Python solid software engineering fundamentals testing code review design patterns performance optimization Strong data foundations SQL data pipelines data modeling and familiarity with Azure data services Data Factory Databricks Synapse Fabric Cosmos DB or equivalent Excellent communication skills with the ability to explain complex AI concepts to technical and nontechnical audiencesPreferred Qualifications Microsoft certifications AI102 Azure AI Engineer Associate DP100 Azure Data Scientist Associate or AZ305 Experience with agentic AI patterns multiagent orchestration and toolfunction calling Azure AI Agent Service Semantic Kernel agents AutoGen or similar Experience with vector databases beyond Azure AI Search eg Cosmos DB vector search PostgreSQL pgvector Pinecone Weaviate Background in NLP computer
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