Lead ML Architect – Google Cloud

Job not on LinkedIn

🔥 0 minutes ago

🇵🇱 Poland – Remote

💵 zł160 - zł190 / hour

⏰ Full Time

🟠 Senior

🏛️ Architect

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Spyrosoft

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2016

🚘 Automotive

🏥 Healthcare

💼 Consulting

Automotive • Healthcare • Consulting

Spyrosoft is a European technology services and software engineering company that partners with enterprises to deliver digital transformation, custom software, and IT consulting. It provides cloud, AI/ML, embedded and IoT development, cybersecurity, product design, managed services, and staff augmentation, and builds platforms and products such as DebtPro, Allpro, and VoicePro. Spyrosoft serves clients across multiple sectors including automotive, healthcare and life sciences, financial services, geospatial, media and entertainment, robotics and energy, and operates from offices across Europe, the UK, and the US.

📋 Description

• Own the target architecture of enterprise ML and MLOps platforms on Google Cloud • Lead architecture workshops, discovery sessions and technical discussions with enterprise customers • Translate business, ML, data, security and operational requirements into scalable architecture designs • Design end-to-end Machine Learning lifecycle patterns covering data preparation, feature engineering, experimentation, model training, evaluation, model registration and promotion • Define architecture standards and best practices for Vertex AI and Gemini Enterprise Agent Platform Pipelines • Design reusable and modular pipeline architectures supporting multiple teams and ML workloads • Define governance frameworks including approval processes, lifecycle policies, lineage tracking and reproducibility standards • Design feature management solutions leveraging BigQuery and Google Cloud-native services • Establish integration patterns between ML platforms, enterprise data ecosystems, CI/CD frameworks, IAM and governance solutions • Define monitoring strategies covering model performance, drift detection, observability and ground-truth validation • Provide architectural guidance to MLOps, Cloud and Data Engineering teams throughout implementation • Drive technical decision-making and ensure alignment with enterprise architecture standards • Maintain architecture documentation, technology roadmaps and implementation guidelines • Promote engineering excellence and adoption of cloud-native Machine Learning best practices across the organization

🎯 Requirements

• Proven experience designing and delivering production-grade ML and MLOps platforms in enterprise environments • Deep hands-on expertise with Google Cloud Platform (GCP) • Strong production experience with Vertex AI and Gemini Enterprise Agent Platform Pipelines • Proven ability to design end-to-end ML lifecycle architectures and pipeline orchestration frameworks • Strong understanding of modular, reusable and scalable ML pipeline design patterns • Extensive knowledge of BigQuery and its role within enterprise-scale Machine Learning ecosystems • Practical experience with ML lifecycle management, model monitoring, retraining strategies, rollback mechanisms and reproducibility standards • Hands-on experience designing CI/CD and deployment patterns for Machine Learning solutions • Experience building solutions that operate across development, testing and production environments • Strong understanding of cloud security, IAM, governance and compliance principles • Solid software engineering background with architectural mindset • Ability to evaluate architecture trade-offs related to scalability, security, maintainability, operability and cost optimization • Experience working directly with enterprise customers and senior technical stakeholders • Excellent communication skills and the ability to facilitate architecture workshops and technical discussions • Ability to operate comfortably between strategic architecture planning and implementation-level engineering details • Fluent English (C1) • Nice to have: Google Cloud Professional Cloud Architect certification • Nice to have: Google Cloud Professional Machine Learning Engineer certification • Nice to have: Experience with AI and Generative AI platforms deployed in enterprise environments • Nice to have: Knowledge of Responsible AI, model governance and enterprise AI adoption frameworks • Nice to have: Experienced in using AI tools in day-to-day workflow