Mindera
Computer Software
MachineLearningArchitect
Neural analysis suggests this role is
optimal for Senior candidates.
“Machine Learning Architect at Mindera. Skills: Machine Learning Architect, AI and ML solutions, Cloud data platforms, Databricks, MLOps, Scalable data architectures, AI platform governance, Apache Spark, Python, SQL, Cloud platforms (AWS, Azure, or GCP), Apache Airflow. Define and lead the architecture for scalable Machine Learning and AI platforms. Design end-to-end ML workflows using Databricks, including: Feature engineering, Model training, Experimentation, Deployment, Monitoring”
What You'll Achieve.
Enable enterprise-scale machine learning capabilities; Design robust, scalable, and production-ready AI solutions; Optimize data models and feature stores; Drive cost optimization, scalability, and operational excellence across ML platforms
Industry & Context.
Translate business needs into scalable technical solutions
What They're Looking For.
Must Have
8+ years in Data, AI, or Machine Learning Engineering roles, 3+ years designing ML platforms or AI architecture at scale, Databricks, Apache Spark, Python, SQL, MLOps, ML lifecycle management, Distributed ML systems, Feature engineering, Model deployment patterns, Databricks Unity Catalog, Delta Lake, Lakehouse architecture, Cloud platforms (AWS, Azure, or GCP), Deploying ML models into production environments, Data architecture, Scalable ETL/ELT patterns, Apache Airflow
Nice to Have
LLMs, Vector databases, Retrieval augmented generation (RAG), AI agents
What You'll Do.
Define and lead the architecture for scalable Machine Learning and AI platforms
Design end-to-end ML workflows using Databricks
including: Feature engineering
Architect scalable data pipelines for AI/ML workloads using: Apache Spark
Establish MLOps best practices including: CI/CD for ML
Observability and monitoring
Design secure and compliant AI architectures aligned with governance and privacy standards
Partner with Data Engineering teams to optimize data models and feature stores
Guide Data Scientists and ML Engineers on scalable production design patterns
Evaluate and integrate modern AI capabilities
including (this will be a plus): LLMs
Retrieval augmented generation (RAG)
Drive cost optimization
and operational excellence across ML platforms
Define reference architectures and best practices across multiple ML teams (not just owning a single project)
Support stakeholder engagement and translate business needs into scalable technical solutions
How You'll Work.
Team & Collaboration
Highly collaborative role working closely with Data Engineering, Data Science, Product, and Business stakeholders; Partner with Data Engineering teams; Guide Data Scientists and ML Engineers; Define reference architectures and best practices across multiple ML teams
Communication Scope
Stakeholder communication
Full Job Description
We are looking for an experienced **Machine Learning Architect** to lead the design and implementation of scalable AI and ML solutions across modern cloud data platforms. This role combines architecture, engineering, and strategic leadership to enable enterprise-scale machine learning capabilities. The ideal candidate has strong hands-on experience with Databricks and a deep understanding of ML lifecycle management, MLOps, scalable data architectures, and AI platform governance. This is a highly collaborative role working closely with Data Engineering, Data Science, Product, and Business stakeholders to design robust, scalable, and production-ready AI solutions. This role has the responsabilities to: * Define and lead the architecture for scalable Machine Learning and AI platforms. * Design end-to-end ML workflows using Databricks, including: Feature engineering, Model training, Experimentation, Deployment, Monitoring * Architect scalable data pipelines for AI/ML workloads using:, Apache Spark, Python, SQL * Establish MLOps best practices including:, CI/CD for ML, Model versioning, Model governance, Automated retraining, Model drifting, Observability and monitoring * Design secure and compliant AI architectures aligned with governance and privacy standards. * Partner with Data Engineering teams to optimize data models and feature stores. * Guide Data Scientists and ML Engineers on scalable production design patterns. * Evaluate and integrate modern AI capabilities, including (this will be a plus): LLMs, Vector databases, Retrieval augmented generation (RAG), AI agents * Drive cost optimization, scalability, and operational excellence across ML platforms. * Define reference architectures and best practices across multiple ML teams (not just owning a single project). * Support stakeholder engagement and translate business needs into scalable technical solutions. **Requirements** * 8+ years in Data, AI, or Machine Learning Engineering roles. * 3+ years designing ML pla
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