Principal AI Platform Engineer & MLOps Architect

Das ist der Job

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

Darum lohnt es sich

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

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. 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.

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.

Responsibilities 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. 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. 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. 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. 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.

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