Principal AI Platform Engineer & MLOps Architect

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗿𝗼𝗹𝗲:

We are looking for a highly senior Principal AI Platform Engineer & MLOps Architect (Azure) to help shape and build the bank’s core AI platform capabilities from the ground up. This role will partner closely with data science and data platform teams to transform an already mature and governed Azure ecosystem into a scalable, enterprise-grade Cognitive Data Platform for real-time and generative AI use cases.

𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲:

• 7+ years of experience in Data Engineering, Cloud Architecture, Platform Engineering, or MLOps.

• At least 3 years of recent experience building and productionizing machine learning platforms, inference systems, or LLM infrastructure.

• Proven track record designing platform components such as Feature Stores, vector search backends, or enterprise AI/ML infrastructure from scratch.

• Strong experience delivering production solutions in Microsoft Azure environments.

𝗦𝗸𝗶𝗹𝗹𝘀:

• Deep hands-on knowledge of Azure Machine Learning, including workspaces, managed services, online endpoints, and MLOps patterns.

• Strong experience with Azure AI Search, Azure Cosmos DB vector capabilities, and/or similar vector database technologies.

• Experience with streaming and real-time data technologies such as Azure Event Hubs, Azure Stream Analytics, Azure Functions, and Azure Databricks.

• Solid understanding of Azure Data Lake Storage Gen2, Microsoft Fabric / OneLake, and enterprise data platform integration patterns.

• Strong coding skills in Python and PySpark.

• Proven experience with Infrastructure as Code using Terraform and/or Bicep.

• Good command of CI/CD practices using Azure DevOps and/or GitHub Actions.

• Strong architectural thinking, with ability to combine strategy, hands-on engineering, and delivery ownership.

• Clear communication skills and confidence working with both technical and non-technical stakeholders.

• Fluent in English, spoken and written.

𝗞𝗲𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀:

𝗔𝗜 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 & 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁

• Design and operationalize an enterprise Feature Store within the Azure ecosystem, enabling Data Scientists to discover, version, govern, and reuse features across batch and near-real-time use cases.

• Define the target architecture for offline and online feature serving, with strong focus on consistency, scalability, and low-latency access.

• Mitigate training-serving skew by implementing robust feature materialization and synchronization patterns across analytical and production environments.

• Establish reusable platform standards for feature engineering, feature publishing, and production ML consumption.

𝗩𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵, 𝗥𝗔𝗚 & 𝗟𝗟𝗠𝗢𝗽𝘀

• Architect and scale vector database capabilities for enterprise AI and Generative AI use cases using Azure-native services.

• Design and implement data chunking, embedding, metadata tagging, and retrieval pipelines to support high-quality Retrieval-Augmented Generation (RAG) solutions.

• Evaluate and implement fit-for-purpose patterns across Azure AI Search, Azure Cosmos DB vector capabilities, and related services for semantic and hybrid search.

• Contribute to the operationalization of LLM-backed services with focus on reliability, performance, and governance.

𝗡𝗲𝗮𝗿-𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 & 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲

• Build near-real-time ingestion pipelines using Azure-native streaming services such as Event Hubs, Stream Analytics, and/or Databricks Structured Streaming.

• Design and implement production-grade inference pipelines capable of serving live model predictions with low latency and high throughput.

• Deploy and manage online inference services through Azure Machine Learning endpoints and/or containerized platforms such as AKS.

• Ensure production readiness through monitoring, alerting, resiliency, and scalable deployment patterns.

𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 & 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽

• Act as the lead technical bridge between Data Science, enterprise data governance, and platform engineering teams.

• Translate advanced AI experimentation into modular, secure, and production-ready MLOps solutions.

• Provide architectural direction, engineering standards, and hands-on guidance for AI platform buildout.

• Mentor technical stakeholders on platform best practices, operational excellence, and sustainable delivery models.

𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝘄𝗮𝘆𝘀 𝗼𝗳 𝘄𝗼𝗿𝗸𝗶𝗻𝗴

• Ensure all AI platform components align with enterprise security controls, data classification policies, and governance requirements.

• Apply best practices across secrets management, access control, encryption, auditability, and compliant data usage.

• Promote Infrastructure as Code, CI/CD automation, and repeatable deployment standards across the AI platform stack.

• Work in close collaboration with cross-functional stakeholders in an agile, delivery-focused environment.

𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮:

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