Machine Learning Architect

🕒 May 13

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Mindera

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2014

🤝 B2B

☁️ SaaS

B2B • Consulting • SaaS

Mindera is a software engineering and consulting firm that prioritizes building strong relationships with its clients and communities. The company focuses on delivering custom software solutions while fostering emotional connections with its partners through trust and support. Mindera collaborates with many established brands across various industries, offering B2B services that include technology consulting and education. Their expansion into international markets, like Australia, indicates their global presence and commitment. Mindera emphasizes a people-centric approach, aiming to be a true partner and extension of their clients' teams.

📋 Description

• 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 platforms or AI architecture at scale. • Strong hands-on experience with: • - Databricks • - Apache Spark • - Python • - SQL • Strong understanding of: • - MLOps • - ML lifecycle management • - Distributed ML systems • - Feature engineering • - Model deployment patterns • Databricks Unity Catalog, Delta Lake, and Lakehouse architecture experience. • Experience with cloud platforms (AWS, Azure, or GCP). • Experience deploying ML models into production environments. • Strong knowledge of data architecture and scalable ETL/ELT patterns. • Experience working with orchestration frameworks such as Apache Airflow. • Strong stakeholder communication and technical leadership skills.