AI Engineer Manager

AI Manage

.The ideal candidate combines strong technical leadership with hands-on experience architecting end-to-end AI/ML systems—from data readiness through model deployment—leveraging Azure’s cloud-native and Fabric-based services

.Key Responsibiliti

• esArchitect and lead the design and implementation of AI/ML solutions on Azure Cloud (Azure Machine Learning, Azure Databricks, Synapse, Azure AI Foundry, Microsoft Fabric, Cognitive Services, Azure OpenAI etc.
• ).Define end-to-end AI architecture encompassing data pipelines, feature stores, model training, deployment, and monitorin
• g.Partner with stakeholders to translate business challenges into AI-driven solutions and technical blueprint
• s.Design scalable and secure architectures adhering to best practices in data governance, MLOps, LLMOps, and cost optimizatio
• n.Integrate Microsoft Fabric as the unified data foundation for AI workloads, ensuring governed, high-quality data access and lineage visibilit
• y.Lead MLOps initiatives including model CI/CD, versioning, monitoring, and drift detection using Azure DevOps, Azure ML Pipelines, and Azure AI Foundr
• y.Evaluate and implement Generative AI (GenAI) and LLM-based solutions using Azure OpenAI, Cognitive Services, and frameworks such as LangChain or LangGrap
• h.Establish observability and monitoring frameworks using Azure Monitor, Application Insights, MLflow, and Databricks dashboards for AI workload
• s.Collaborate with cross-functional teams—Data Engineers, Data Scientists, and DevOps—to ensure seamless integration and delivery of AI product
• s.Provide technical mentorship and architectural guidance to engineering team
• s.Stay current with emerging trends in AI/ML, cloud technologies, and GenAI advancement

s.Qualificati

• ons8+ years of overall IT experience, including 4+ years in AI/ML solutioning and 3 + years in Azure-based architectu
• re.Deep expertise in the Azure AI ecosystem—Azure Machine Learning, Azure Databricks, Azure AI Foundry, Microsoft Fabric, Azure Cognitive Services, Azure OpenAI, Azure Synapse, and Data La
• ke.Strong proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-lear
• n).Proven experience with transformer model architectures and practical understanding of LLM specifics like context handli
• ng.Proven experience designing, implementing, and optimising prompt strategies (e.g., chaining, templates, dynamic inputs); practical understanding of output post-processi
• ng.Must have hands-on experience implementing and automating MLOps/LLMOps practices, including model tracking, versioning, deployment, monitoring (latency, cost, throughput, reliability), logging, and retraining workflo
• ws.Must have worked extensively with MLOps/experiment tracking and operational tools (e.g., MLflow, Weights & Biases) and have a demonstrable track reco
• rd.Proven ability to monitor, evaluate, and optimise AI/LLM solutions for performance (latency, throughput, reliability), accuracy, and cost in production environmen
• ts.Proven experience in MLOps and LLMOps design and automation using Azure DevOps, Docker, and Kubernet
• es.Hands-on experience integrating AI solutions with Microsoft Fabric and Azure Data Factory for data preparation and governan
• ce.Strong understanding of distributed systems, model lifecycle management, and AI system scalabili
• ty.Experience in LLM fine-tuning, prompt engineering, or AI solution integration with enterprise applicatio
• ns.Excellent communication and stakeholder management skills with a strategic minds