# Machine Learning Architect

**Company:** [Mindera](http://jobs.workable.com/companies/kAFiuZuCGfsouMaZWJDxvE.md)
**Location:** Remote
**Workplace:** remote
**Department:** Engineering

[Apply for this job](http://jobs.workable.com/view/0109906f-e52f-49db-9ff2-ff92d391678c)

## 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 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.