AI Engineering Manager

AI Manager

Key Responsibilities

• Architect 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 monitoring.
• Partner with stakeholders to translate business challenges into AI-driven solutions and technical blueprints.
• Design scalable and secure architectures adhering to best practices in data governance, MLOps, LLMOps, and cost optimization.
• Integrate Microsoft Fabric as the unified data foundation for AI workloads, ensuring governed, high-quality data access and lineage visibility.
• Lead MLOps initiatives including model CI/CD, versioning, monitoring, and drift detection using Azure DevOps, Azure ML Pipelines, and Azure AI Foundry.
• Contribute to the design, building, or working with event-driven architectures and relevant for asynchronous processing and system integration
• Experience developing and deploying LLM-powered features into production systems, translating experimental outputs into robust services with clear APIs.
• Experience working within a standard software development lifecycle
• Evaluate and implement Generative AI (GenAI) and LLM-based solutions using Azure OpenAI, Cognitive Services, and frameworks such as LangChain or LangGraph.
• Establish observability and monitoring frameworks using Azure Monitor, Application Insights, MLflow, and Databricks dashboards for AI workloads.
• Collaborate with cross-functional teams Data Engineers, Data Scientists, Software Engineers and DevOps to ensure seamless integration and delivery of AI products.
• Provide technical mentorship and architectural guidance to engineering teams.Qualifications

• 7 + years of overall IT experience, including 4+ years in AI/ML solutioning and 3 + years in Azure-based architecture.
• 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 Lake.
• Strong proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Proven experience with transformer model architectures and practical understanding of LLM specifics like context handling.
• Proven experience designing, implementing, and optimising prompt strategies (e.g., chaining, templates, dynamic inputs); practical understanding of output post-processing.
• Must have hands-on experience implementing and automating MLOps/LLMOps practices, including model tracking, versioning, deployment, monitoring (latency, cost, throughput, reliability), logging, and retraining workflows.
• Must have worked extensively with MLOps/experiment tracking and operational tools (e.g., MLflow, Weights & Biases) and have a demonstrable track record.
• Proven ability to monitor, evaluate, and optimise AI/LLM solutions for performance (latency, throughput, reliability), accuracy, and cost in production environments.
• Proven experience in MLOps and LLMOps design and automation using Azure DevOps, Docker, and Kubernetes.
• Hands-on experience integrating AI solutions with Microsoft Fabric and Azure Data Factory for data preparation and governance.
• Strong understanding of distributed systems, model lifecycle management, and AI system scalability.
• Experience in LLM fine-tuning, prompt engineering, or AI solution integration with enterprise applications.
• Excellent communication and stakeholder management skills with a strategic mindset