Principal AI Platform Engineer & MLOps Architect

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. /p h3Qualifications /h3 ul li7+ years of experience in Data Engineering, Cloud Architecture, Platform Engineering, or MLOps. /li liAt least 3 years of recent experience building and productionizing machine learning platforms, inference systems, or LLM infrastructure. /li liProven track record designing platform components such as Feature Stores, vector search backends, or enterprise AI/ML infrastructure from scratch. /li liStrong experience delivering production solutions in Microsoft Azure environments. /li liDeep hands‑on knowledge of Azure Machine Learning, including workspaces, managed services, online endpoints, and MLOps patterns. /li liStrong experience with Azure AI Search, Azure Cosmos DB vector capabilities, and/or similar vector database technologies. /li liExperience with streaming and real-time data technologies such as Azure Event Hubs, Azure Stream Analytics, Azure Functions, and Azure Databricks. /li liSolid understanding of Azure Data Lake Storage Gen2, Microsoft Fabric / OneLake, and enterprise data platform integration patterns. /li liStrong coding skills in Python and PySpark. /li liProven experience with Infrastructure as Code using Terraform and/or Bicep. /li liGood command of CI/CD practices using Azure DevOps and/or GitHub Actions. /li liStrong architectural thinking, with ability to combine strategy, hands‑on engineering, and delivery ownership. /li liClear communication skills and confidence working with both technical and non-technical stakeholders. /li liFluent in English, spoken and written. /li /ul h3Responsibilities /h3 ul liDesign 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. /li liDefine the target architecture for offline and online feature serving, with strong focus on consistency, scalability, and low-latency access. /li liMitigate training-serving skew by implementing robust feature materialization and synchronization patterns across analytical and production environments. /li liEstablish reusable platform standards for feature engineering, feature publishing, and production ML consumption. /li liArchitect and scale vector database capabilities for enterprise AI and Generative AI use cases using Azure-native services. /li liDesign and implement data chunking, embedding, metadata tagging, and retrieval pipelines to support high-quality Retrieval-Augmented Generation (RAG) solutions. /li liEvaluate and implement fit‑for‑purpose patterns across Azure AI Search, Azure Cosmos DB vector capabilities, and related services for semantic and hybrid search. /li liContribute to the operationalization of LLM-backed services with focus on reliability, performance, and governance. /li liBuild near-real-time ingestion pipelines using Azure-native streaming services such as Event Hubs, Stream Analytics, and/or Databricks Structured Streaming. /li liDesign and implement production-grade inference pipelines capable of serving live model predictions with low latency and high throughput. /li liDeploy and manage online inference services through Azure Machine Learning endpoints and/or containerized platforms such as AKS. /li liEnsure production readiness through monitoring, alerting, resiliency, and scalable deployment patterns. /li liAct as the lead technical bridge between Data Science, enterprise data governance, and platform engineering teams. /li liTranslate advanced AI experimentation into modular, secure, and production-ready MLOps solutions. /li liProvide architectural direction, engineering standards, and hands‑on guidance for AI platform buildout. /li liMentor technical stakeholders on platform best practices, operational excellence, and sustainable delivery models. /li liEnsure all AI platform components align with enterprise security controls, data classification policies, and governance requirements. /li liApply best practices across secrets management, access control, encryption, auditability, and compliant data usage. /li liPromote Infrastructure as Code, CI/CD automation, and repeatable deployment standards across the AI platform stack. /li liWork in close collaboration with cross‑functional stakeholders in an agile, delivery-focused environment. /li /ul pBy submitting your resume, you agree to the retention and use of your personal data by TSG for recruitment purposes, including sharing with our clients in the context of your application. /p /p #J-18808-Ljbffr ppWe 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.