Senior AI Engineer

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.

Desired Candidate Profile

Required Qualifications 8 10+ years of overall experience in AI, machine learning, and data engineering, with a strong track record of delivering production systems. Minimum 2 3 years of hands-on 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, fine-tuning (LoRA/PEFT), quantization, and evaluation methods. Practical experience with open-source models and frameworks (Hugging Face, Llama, Mistral, Phi, vLLM/ONNX Runtime or similar serving stacks). Proven MLOps/LLMOps

Experience

CI/CD, model versioning, automated testing and evaluation, monitoring, and rollback strategies. Experience designing and building microservices and APIs (containers, Kubernetes, REST/gRPC, event-driven 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 AI/ML services (e.g., Amazon Bedrock, SageMaker, Vertex AI). Expert-level 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 non-technical audiences. Preferred Qualifications Microsoft certifications: AI-102 (Azure AI Engineer Associate), DP-100 (Azure Data Scientist Associate), or AZ-305. Experience with agentic AI patterns, multi-agent orchestration, and tool/function calling (Azure AI Agent Service, Semantic Kernel agents, AutoGen, or similar). Experience with vector databases beyond Azure AI Search (e.g., Cosmos DB vector search, PostgreSQL pgvector, Pinecone, Weaviate). Background in NLP, computer vision, or speech systems prior to the GenAI era. Experience with model evaluation and observability tooling (Azure AI evaluation SDK, Prompt Flow evaluations, Langfuse, MLflow, Weights & Biases). Familiarity with regulatory and compliance frameworks relevant to AI (GDPR, ISO/IEC 42001, NIST AI RMF, EU AI Act, or regional data protection regulations). Experience operating in regulated industries (financial services, healthcare, government) or large-enterprise environments. Contributions to open-source projects, publications, or technical community engagement Key Competencies Ownership and delivery focus in ambiguous, fast-moving environments Systems thinking: balancing model quality, latency, cost, security, and maintainability Pragmatism about when GenAI is (and is not) the right tool Mentorship and collaborative technical leadership