AI Engineer Manager

AI Engineer Manager - Hyderabad, TS

Blend360 is seeking an experienced AI Manager to lead the design and implementation of enterprise-grade AI solutions, with a strong focus on the Azure AI, Microsoft Fabric, and Machine Learning ecosystems. This role requires a blend of technical leadership and hands-on experience in architecting end-to-end AI/ML systems, from data preparation to model deployment, utilizing Azure's cloud-native services and Microsoft Fabric.

Key Responsibilities

• Architect, lead the design, and implement AI/ML solutions on Azure Cloud, including Azure Machine Learning, Azure Databricks, Synapse, Azure AI Foundry, Microsoft Fabric, Cognitive Services, and Azure OpenAI.
• Define comprehensive AI architectures covering data pipelines, feature stores, model training, deployment, and monitoring.
• Collaborate with stakeholders to translate business challenges into actionable 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 central data foundation for AI workloads, ensuring governed data access and clear lineage.
• Lead MLOps initiatives, including model CI/CD, versioning, monitoring, and drift detection, utilizing Azure DevOps, Azure ML Pipelines, and Azure AI Foundry.
• Contribute to the design and development of event-driven architectures for asynchronous processing and system integration.
• Develop and deploy LLM-powered features into production systems, transforming experimental outputs into robust services with defined APIs.
• Operate within a standard software development lifecycle.
• Evaluate and implement Generative AI (GenAI) and LLM-based solutions using Azure OpenAI, Cognitive Services, and frameworks like LangChain or LangGraph.
• Establish observability and monitoring frameworks for AI workloads using Azure Monitor, Application Insights, MLflow, and Databricks dashboards.
• Collaborate with cross-functional teams, including Data Engineers, Data Scientists, Software Engineers, and DevOps professionals, to ensure the seamless integration and delivery of AI products.
• Provide technical mentorship and architectural guidance to engineering teams.

Qualifications

• Minimum 7 years of overall IT experience, with at least 4 years focused on AI/ML solutioning and 3 years in Azure-based architecture.
• In-depth expertise in the Azure AI ecosystem, including Azure Machine Learning, Azure Databricks, Azure AI Foundry, Microsoft Fabric, Azure Cognitive Services, Azure OpenAI, Azure Synapse, and Azure Data Lake.
• Strong proficiency in Python and major ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• Proven experience with transformer model architectures and a practical understanding of LLM specifics, including context handling.
• Demonstrated experience in designing, implementing, and optimizing prompt strategies (e.g., chaining, templates, dynamic inputs) and a practical understanding of output post-processing.
• Hands-on experience implementing and automating MLOps/LLMOps practices, including model tracking, versioning, deployment, monitoring (latency, cost, throughput, reliability), logging, and retraining workflows.
• Extensive experience with MLOps/experiment tracking and operational tools like MLflow and Weights & Biases, with a demonstrable track record of success.
• Proven ability to monitor, evaluate, and optimize AI/LLM solutions for performance (latency, throughput, reliability), accuracy, and cost in production environments.
• Significant 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 integrating AI solutions with enterprise applications.
• Excellent communication and stakeholder management skills, coupled with a strategic mindset.