Ai Manager

Company Description

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit

Job Description We are seeking an experienced AI Manager with deep expertise in Azure AI, Microsoft Fabric, and Machine Learning ecosystems to design and implement enterprise-grade AI solutions. 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 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. Stay current with emerging trends in AI/ML, cloud technologies, and GenAI advancements.

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 understandingof 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. Required Collaboration Model Proven ability to effectively collaborate across functions: With Data Scientists on model fine-tuning, prompt design, and experimental evaluation. With Product and Software Engineers for integrating AI capabilities into platforms and user-facing services. With DevOps/Platform Engineers for environment setup, monitoring, infrastructure, and reliability. With Data Engineering for designing and accessing upstream data pipelines.